Prepare data of Analysis, Treat Missing Data, Wrong Data, Outlier Data

Import Libraries

import pandas as pd
import numpy as np

from sklearn.linear_model import LinearRegression, Lasso, Ridge, LogisticRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor, plot_tree
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report,recall_score,f1_score,precision_score

# import tensorflow as tf
# import keras

import seaborn as sns
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')

Read The Datasets

df_attr = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')

pd.options.display.max_columns=200

df_attr.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

# pd.options.display.max_columns=200

df_attr.shape

(1470, 35)

Data Visulization

# Attrition

print(pd.DataFrame(df_attr['Attrition'].value_counts()))
print('----------------------------------------------')

print('Yes      :', round((237/(1233+237)*100),2),'%')
print('No       :', round((1233/(1233+237)*100),2),'%')

sns.countplot(x=df_attr['Attrition'])

   Attrition
0       1233
1        237
----------------------------------------------
Yes      : 16.12 %
No       : 83.88 %

<AxesSubplot: xlabel='Attrition', ylabel='count'>

# Age
plt.figure(figsize=(20,6))
sns.countplot(x=df_attr['Age'],hue = df_attr['Attrition'])

<AxesSubplot: xlabel='Age', ylabel='count'>

# Business Travel

print(pd.DataFrame(df_attr['BusinessTravel'].value_counts()))
print('----------------------------------------------------')

print('BusinessTravel     :    ', round(1043/(1043+277+150)*100,2),'%')
print('Travel_Frequently  :    ', round(277/(1043+277+150)*100,2),'%')
print('Non-Travel         :    ', round(150/(1043+277+150)*100,2),'%')

sns.countplot(x=df_attr['BusinessTravel'], hue = df_attr['Attrition'])

                   BusinessTravel
Travel_Rarely                1043
Travel_Frequently             277
Non-Travel                    150
----------------------------------------------------
BusinessTravel     :     70.95 %
Travel_Frequently  :     18.84 %
Non-Travel         :     10.2 %

<AxesSubplot: xlabel='BusinessTravel', ylabel='count'>

# Department
plt.figure(figsize=(8,4))

print(pd.DataFrame(df_attr['Department'].value_counts()))
print('----------------------------------------------')

print(pd.DataFrame(df_attr['Department'].value_counts(normalize=True)))

sns.countplot(x=df_attr['Department'], hue = df_attr['Attrition'])

                        Department
Research & Development         961
Sales                          446
Human Resources                 63
----------------------------------------------
                        Department
Research & Development    0.653741
Sales                     0.303401
Human Resources           0.042857

<AxesSubplot: xlabel='Department', ylabel='count'>

# EducationField
plt.figure(figsize=(10,4))

print(pd.DataFrame(df_attr['EducationField'].value_counts()))
print('--------------------------------------------------')

print(pd.DataFrame(df_attr['EducationField'].value_counts(normalize=True)))

sns.countplot(x=df_attr['EducationField'], hue = df_attr['Attrition'])

                  EducationField
Life Sciences                606
Medical                      464
Marketing                    159
Technical Degree             132
Other                         82
Human Resources               27
--------------------------------------------------
                  EducationField
Life Sciences           0.412245
Medical                 0.315646
Marketing               0.108163
Technical Degree        0.089796
Other                   0.055782
Human Resources         0.018367

<AxesSubplot: xlabel='EducationField', ylabel='count'>

# Gender

print(pd.DataFrame(df_attr['Gender'].value_counts()))
print('------------------------------------------')
print(pd.DataFrame(df_attr['Gender'].value_counts(normalize=True)))
sns.countplot(x=df_attr['Gender'], hue = df_attr['Attrition'])

        Gender
Male       882
Female     588
------------------------------------------
        Gender
Male       0.6
Female     0.4

<AxesSubplot: xlabel='Gender', ylabel='count'>

# MaritalStatus

print(pd.DataFrame(df_attr['MaritalStatus'].value_counts()))
print('------------------------------------------------')

print(pd.DataFrame(df_attr['MaritalStatus'].value_counts(normalize=True)))

sns.countplot(x=df_attr['MaritalStatus'],hue=df_attr['Attrition'])

          MaritalStatus
Married             673
Single              470
Divorced            327
------------------------------------------------
          MaritalStatus
Married        0.457823
Single         0.319728
Divorced       0.222449

<AxesSubplot: xlabel='MaritalStatus', ylabel='count'>

# Overtime

print(pd.DataFrame(df_attr['OverTime'].value_counts()))
print('-----------------------------------------')

print(pd.DataFrame(df_attr['OverTime'].value_counts(normalize=True)))

sns.countplot(x = df_attr['OverTime'], hue = df_attr['Attrition'])

     OverTime
No       1054
Yes       416
-----------------------------------------
     OverTime
No   0.717007
Yes  0.282993

<AxesSubplot: xlabel='OverTime', ylabel='count'>

df_attr.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Attrition                 1470 non-null   int64 
 1   Age                       1470 non-null   int64 
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(27), object(8)
memory usage: 402.1+ KB

Missing Data Treatment

df_attr.isna().sum()

Attrition                   0
Age                         0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64

Seprate Categorical and Continiues columns

cat = []
con = []
for i in df_attr.columns:
    if (df_attr[i].dtypes == "object"):
        cat.append(i)
    else:
        con.append(i)

cat

['BusinessTravel',
 'Department',
 'EducationField',
 'Gender',
 'JobRole',
 'MaritalStatus',
 'Over18',
 'OverTime']

con

['Attrition',
 'Age',
 'DailyRate',
 'DistanceFromHome',
 'Education',
 'EmployeeCount',
 'EmployeeNumber',
 'EnvironmentSatisfaction',
 'HourlyRate',
 'JobInvolvement',
 'JobLevel',
 'JobSatisfaction',
 'MonthlyIncome',
 'MonthlyRate',
 'NumCompaniesWorked',
 'PercentSalaryHike',
 'PerformanceRating',
 'RelationshipSatisfaction',
 'StandardHours',
 'StockOptionLevel',
 'TotalWorkingYears',
 'TrainingTimesLastYear',
 'WorkLifeBalance',
 'YearsAtCompany',
 'YearsInCurrentRole',
 'YearsSinceLastPromotion',
 'YearsWithCurrManager']

df_train_cat = cat
df_train_cat = df_attr[cat]
df_train_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

# Visulization of categorical columns

plt.figure(figsize=(15,19))
for x1, i in enumerate(df_train_cat.columns):
    if df_train_cat[i].dtypes=='object':
        plt.subplot(3,3,x1+1)
        sns.countplot(x=df_train_cat[i])

df_train_con = con
df_train_con = df_attr[con]
df_train_con

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 27 columns

# Visualisation of continious columns

plt.figure(figsize=(15,19))
for x1, i in enumerate(df_train_con.columns):
    if df_train_con[i].dtypes=='int64' or df_train_con[i].dtypes=='float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con[i])

Removing Outliers

for i in df_train_con.columns:
    q1 = df_train_con[i].quantile(0.25)
    q3 = df_train_con[i].quantile(0.75)
    IQR = q3-q1
    uppertail = q3+1.5*IQR
    lowertail = q1-1.5*IQR
    df_train_con.loc[(df_train_con[i]>uppertail) | (df_train_con[i]<lowertail)]
    mean_1 = df_train_con[i].mean()
    df_train_con.loc[(df_train_con[i]>uppertail) | (df_train_con[i]<lowertail),i]=mean_1

plt.figure(figsize=(15,19))
for x1, i in enumerate(df_train_con.columns):
    if df_train_con[i].dtypes=='int64' or df_train_con[i].dtypes=='float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con[i])

Checking Skewness

df_attr.skew().sort_values()

WorkLifeBalance            -0.552480
JobInvolvement             -0.498419
JobSatisfaction            -0.329672
EnvironmentSatisfaction    -0.321654
RelationshipSatisfaction   -0.302828
Education                  -0.289681
HourlyRate                 -0.032311
DailyRate                  -0.003519
EmployeeCount               0.000000
StandardHours               0.000000
EmployeeNumber              0.016574
MonthlyRate                 0.018578
Age                         0.413286
TrainingTimesLastYear       0.553124
PercentSalaryHike           0.821128
YearsWithCurrManager        0.833451
YearsInCurrentRole          0.917363
DistanceFromHome            0.958118
StockOptionLevel            0.968980
JobLevel                    1.025401
NumCompaniesWorked          1.026471
TotalWorkingYears           1.117172
MonthlyIncome               1.369817
YearsAtCompany              1.764529
Attrition                   1.844366
PerformanceRating           1.921883
YearsSinceLastPromotion     1.984290
dtype: float64

Check Correlation

plt.figure(figsize=(20,20))
sns.heatmap(df_attr.corr(), annot=True)

<AxesSubplot: >

2. Problem Statement

# Create new/derived predictors (e.g Age group) for analysis

df_attr.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

df_attr['Age'].unique()

array([41, 49, 37, 33, 27, 32, 59, 30, 38, 36, 35, 29, 31, 34, 28, 22, 53,
       24, 21, 42, 44, 46, 39, 43, 50, 26, 48, 55, 45, 56, 23, 51, 40, 54,
       58, 20, 25, 19, 57, 52, 47, 18, 60], dtype=int64)

df_attr['Age'].value_counts()

35    78
34    77
31    69
36    69
29    68
32    61
30    60
33    58
38    58
40    57
37    50
27    48
28    48
42    46
39    42
45    41
41    40
26    39
46    33
44    33
43    32
50    30
24    26
25    26
47    24
49    24
55    22
48    19
51    19
53    19
52    18
54    18
22    16
56    14
58    14
23    14
21    13
20    11
59    10
19     9
18     8
60     5
57     4
Name: Age, dtype: int64

df_attr['Age'].max()

60

T = []
for i in df_attr['Age']:
    if(i>15 and i<=30):
        T.append('GROUP1')
        
    elif(i>30 and i<=45):
        T.append('GROUP2')
        
    else:
        T.append('GROUP3')

df_attr['AGE_GROUP']=T

df_attr.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager	AGE_GROUP
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5	GROUP2
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7	GROUP3
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0	GROUP2
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0	GROUP2
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2	GROUP1

3. Problem Statement

# 3. Explore the Data using Exploratory Data Analysis - for Y and all Xs

df_attr3 = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')
df_attr3.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

# Seprate Categorical and continious columns

cat = []
con = []
for i in df_attr3.columns:
    if (df_attr3[i].dtypes == 'object'):
        cat.append(i)
    else:
        con.append(i)

df_attr3_cat = cat
df_attr3_cat = df_attr3[cat]
df_attr3_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

df_attr3_con = con
df_attr3_con = df_attr3[con]
df_attr3_con

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 27 columns

# Visualisation of categorical columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_attr3_cat.columns):
    if df_attr3_cat[i].dtypes == 'object':
        plt.subplot(3,3,x1+1)
        sns.countplot(x=df_attr3_cat[i])

# Visulisation of continious columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_attr3_con.columns):
    if df_attr3_con[i].dtypes == 'int64' or df_attr3_con[i].dtypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_attr3_con[i])

Removing Outliers

df_attr3.describe()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	0.161224	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	2.728571	6502.931293	14313.103401	2.693197	15.209524	3.153741	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	0.367863	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	1.102846	4707.956783	7117.786044	2.498009	3.659938	0.360824	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	0.000000	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	1.000000	1009.000000	2094.000000	0.000000	11.000000	3.000000	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	2.000000	2911.000000	8047.000000	1.000000	12.000000	3.000000	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	0.000000	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	3.000000	4919.000000	14235.500000	2.000000	14.000000	3.000000	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	0.000000	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	4.000000	8379.000000	20461.500000	4.000000	18.000000	3.000000	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	1.000000	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	4.000000	19999.000000	26999.000000	9.000000	25.000000	4.000000	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

for i in df_attr3_con.columns:
    q1 = df_attr3_con[i].quantile(0.25)
    q3 = df_attr3_con[i].quantile(0.75)
    IQR = q3-q1
    uppertail = q3+1.5*IQR
    lowertail = q1-1.5*IQR
    df_attr3_con.loc[(df_attr3_con[i]>uppertail) | (df_attr3_con[i]<lowertail)]
    mean_1 = df_attr3_con[i].mean()
    df_attr3_con.loc[(df_attr3_con[i]>uppertail) | (df_attr3_con[i]<lowertail),i]=mean_1
    

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_attr3_con.columns):
    if df_attr3_con[i].dtypes == 'int64' or df_attr3_con[i].dtypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_attr3_con[i])

Checking Skewness

df_attr3.skew().sort_values()

WorkLifeBalance            -0.552480
JobInvolvement             -0.498419
JobSatisfaction            -0.329672
EnvironmentSatisfaction    -0.321654
RelationshipSatisfaction   -0.302828
Education                  -0.289681
HourlyRate                 -0.032311
DailyRate                  -0.003519
EmployeeCount               0.000000
StandardHours               0.000000
EmployeeNumber              0.016574
MonthlyRate                 0.018578
Age                         0.413286
TrainingTimesLastYear       0.553124
PercentSalaryHike           0.821128
YearsWithCurrManager        0.833451
YearsInCurrentRole          0.917363
DistanceFromHome            0.958118
StockOptionLevel            0.968980
JobLevel                    1.025401
NumCompaniesWorked          1.026471
TotalWorkingYears           1.117172
MonthlyIncome               1.369817
YearsAtCompany              1.764529
Attrition                   1.844366
PerformanceRating           1.921883
YearsSinceLastPromotion     1.984290
dtype: float64

Checking Correlation

df_attr3.corr()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
Attrition	1.000000	-0.159205	-0.056652	0.077924	-0.031373	NaN	-0.010577	-0.103369	-0.006846	-0.130016	-0.169105	-0.103481	-0.159840	0.015170	0.043494	-0.013478	0.002889	-0.045872	NaN	-0.137145	-0.171063	-0.059478	-0.063939	-0.134392	-0.160545	-0.033019	-0.156199
Age	-0.159205	1.000000	0.010661	-0.001686	0.208034	NaN	-0.010145	0.010146	0.024287	0.029820	0.509604	-0.004892	0.497855	0.028051	0.299635	0.003634	0.001904	0.053535	NaN	0.037510	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089
DailyRate	-0.056652	0.010661	1.000000	-0.004985	-0.016806	NaN	-0.050990	0.018355	0.023381	0.046135	0.002966	0.030571	0.007707	-0.032182	0.038153	0.022704	0.000473	0.007846	NaN	0.042143	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363
DistanceFromHome	0.077924	-0.001686	-0.004985	1.000000	0.021042	NaN	0.032916	-0.016075	0.031131	0.008783	0.005303	-0.003669	-0.017014	0.027473	-0.029251	0.040235	0.027110	0.006557	NaN	0.044872	0.004628	-0.036942	-0.026556	0.009508	0.018845	0.010029	0.014406
Education	-0.031373	0.208034	-0.016806	0.021042	1.000000	NaN	0.042070	-0.027128	0.016775	0.042438	0.101589	-0.011296	0.094961	-0.026084	0.126317	-0.011111	-0.024539	-0.009118	NaN	0.018422	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	-0.010577	-0.010145	-0.050990	0.032916	0.042070	NaN	1.000000	0.017621	0.035179	-0.006888	-0.018519	-0.046247	-0.014829	0.012648	-0.001251	-0.012944	-0.020359	-0.069861	NaN	0.062227	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197
EnvironmentSatisfaction	-0.103369	0.010146	0.018355	-0.016075	-0.027128	NaN	0.017621	1.000000	-0.049857	-0.008278	0.001212	-0.006784	-0.006259	0.037600	0.012594	-0.031701	-0.029548	0.007665	NaN	0.003432	-0.002693	-0.019359	0.027627	0.001458	0.018007	0.016194	-0.004999
HourlyRate	-0.006846	0.024287	0.023381	0.031131	0.016775	NaN	0.035179	-0.049857	1.000000	0.042861	-0.027853	-0.071335	-0.015794	-0.015297	0.022157	-0.009062	-0.002172	0.001330	NaN	0.050263	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123
JobInvolvement	-0.130016	0.029820	0.046135	0.008783	0.042438	NaN	-0.006888	-0.008278	0.042861	1.000000	-0.012630	-0.021476	-0.015271	-0.016322	0.015012	-0.017205	-0.029071	0.034297	NaN	0.021523	-0.005533	-0.015338	-0.014617	-0.021355	0.008717	-0.024184	0.025976
JobLevel	-0.169105	0.509604	0.002966	0.005303	0.101589	NaN	-0.018519	0.001212	-0.027853	-0.012630	1.000000	-0.001944	0.950300	0.039563	0.142501	-0.034730	-0.021222	0.021642	NaN	0.013984	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281
JobSatisfaction	-0.103481	-0.004892	0.030571	-0.003669	-0.011296	NaN	-0.046247	-0.006784	-0.071335	-0.021476	-0.001944	1.000000	-0.007157	0.000644	-0.055699	0.020002	0.002297	-0.012454	NaN	0.010690	-0.020185	-0.005779	-0.019459	-0.003803	-0.002305	-0.018214	-0.027656
MonthlyIncome	-0.159840	0.497855	0.007707	-0.017014	0.094961	NaN	-0.014829	-0.006259	-0.015794	-0.015271	0.950300	-0.007157	1.000000	0.034814	0.149515	-0.027269	-0.017120	0.025873	NaN	0.005408	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079
MonthlyRate	0.015170	0.028051	-0.032182	0.027473	-0.026084	NaN	0.012648	0.037600	-0.015297	-0.016322	0.039563	0.000644	0.034814	1.000000	0.017521	-0.006429	-0.009811	-0.004085	NaN	-0.034323	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746
NumCompaniesWorked	0.043494	0.299635	0.038153	-0.029251	0.126317	NaN	-0.001251	0.012594	0.022157	0.015012	0.142501	-0.055699	0.149515	0.017521	1.000000	-0.010238	-0.014095	0.052733	NaN	0.030075	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319
PercentSalaryHike	-0.013478	0.003634	0.022704	0.040235	-0.011111	NaN	-0.012944	-0.031701	-0.009062	-0.017205	-0.034730	0.020002	-0.027269	-0.006429	-0.010238	1.000000	0.773550	-0.040490	NaN	0.007528	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985
PerformanceRating	0.002889	0.001904	0.000473	0.027110	-0.024539	NaN	-0.020359	-0.029548	-0.002172	-0.029071	-0.021222	0.002297	-0.017120	-0.009811	-0.014095	0.773550	1.000000	-0.031351	NaN	0.003506	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827
RelationshipSatisfaction	-0.045872	0.053535	0.007846	0.006557	-0.009118	NaN	-0.069861	0.007665	0.001330	0.034297	0.021642	-0.012454	0.025873	-0.004085	0.052733	-0.040490	-0.031351	1.000000	NaN	-0.045952	0.024054	0.002497	0.019604	0.019367	-0.015123	0.033493	-0.000867
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	-0.137145	0.037510	0.042143	0.044872	0.018422	NaN	0.062227	0.003432	0.050263	0.021523	0.013984	0.010690	0.005408	-0.034323	0.030075	0.007528	0.003506	-0.045952	NaN	1.000000	0.010136	0.011274	0.004129	0.015058	0.050818	0.014352	0.024698
TotalWorkingYears	-0.171063	0.680381	0.014515	0.004628	0.148280	NaN	-0.014365	-0.002693	-0.002334	-0.005533	0.782208	-0.020185	0.772893	0.026442	0.237639	-0.020608	0.006744	0.024054	NaN	0.010136	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188
TrainingTimesLastYear	-0.059478	-0.019621	0.002453	-0.036942	-0.025100	NaN	0.023603	-0.019359	-0.008548	-0.015338	-0.018191	-0.005779	-0.021736	0.001467	-0.066054	-0.005221	-0.015579	0.002497	NaN	0.011274	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096
WorkLifeBalance	-0.063939	-0.021490	-0.037848	-0.026556	0.009819	NaN	0.010309	0.027627	-0.004607	-0.014617	0.037818	-0.019459	0.030683	0.007963	-0.008366	-0.003280	0.002572	0.019604	NaN	0.004129	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759
YearsAtCompany	-0.134392	0.311309	-0.034055	0.009508	0.069114	NaN	-0.011240	0.001458	-0.019582	-0.021355	0.534739	-0.003803	0.514285	-0.023655	-0.118421	-0.035991	0.003435	0.019367	NaN	0.015058	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212
YearsInCurrentRole	-0.160545	0.212901	0.009932	0.018845	0.060236	NaN	-0.008416	0.018007	-0.024106	0.008717	0.389447	-0.002305	0.363818	-0.012815	-0.090754	-0.001520	0.034986	-0.015123	NaN	0.050818	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365
YearsSinceLastPromotion	-0.033019	0.216513	-0.033229	0.010029	0.054254	NaN	-0.009019	0.016194	-0.026716	-0.024184	0.353885	-0.018214	0.344978	0.001567	-0.036814	-0.022154	0.017896	0.033493	NaN	0.014352	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224
YearsWithCurrManager	-0.156199	0.202089	-0.026363	0.014406	0.069065	NaN	-0.009197	-0.004999	-0.020123	0.025976	0.375281	-0.027656	0.344079	-0.036746	-0.110319	-0.011985	0.022827	-0.000867	NaN	0.024698	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000

df_dum_train3 = pd.get_dummies(df_attr3_cat)

df_dum_train3.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0

Standardization

std_scaler = StandardScaler()
std_scaler1 = std_scaler.fit_transform(df_attr3_con)

x3 = pd.DataFrame(std_scaler1,columns=df_attr3_con.columns)

x3.head()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	0.129018	0.726020	2.529583	-1.150554	-0.426230	-1.584178	0.0	-1.018674	-0.374906	0.312607	-2.493820	0.041137	-0.018341	-0.795491	0.294570
1	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.140791	1.488876	-0.672478	2.129306	2.346151	1.191438	0.0	0.510149	-0.057867	0.601911	0.338096	1.069787	0.882230	-0.277632	0.888852
2	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-1.091220	-1.674841	1.614708	-0.057267	-0.426230	-0.658973	0.0	-1.018674	-0.533426	0.601911	0.338096	-1.501837	-1.219103	-0.795491	-1.191138
3	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.835167	1.243211	-0.672478	-1.150554	-0.426230	0.266233	0.0	-1.018674	-0.374906	0.601911	0.338096	0.555462	0.882230	0.758085	-1.191138
4	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.660400	0.325900	0.102053	-0.877232	-0.426230	1.191438	0.0	0.510149	-0.691946	0.601911	0.338096	-0.987512	-0.618722	0.240227	-0.596855

train_merge3 = pd.concat([df_dum_train3,x3],axis=1)

train_merge3.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	0.129018	0.726020	2.529583	-1.150554	-0.426230	-1.584178	0.0	-1.018674	-0.374906	0.312607	-2.493820	0.041137	-0.018341	-0.795491	0.294570
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.140791	1.488876	-0.672478	2.129306	2.346151	1.191438	0.0	0.510149	-0.057867	0.601911	0.338096	1.069787	0.882230	-0.277632	0.888852
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-1.091220	-1.674841	1.614708	-0.057267	-0.426230	-0.658973	0.0	-1.018674	-0.533426	0.601911	0.338096	-1.501837	-1.219103	-0.795491	-1.191138
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.835167	1.243211	-0.672478	-1.150554	-0.426230	0.266233	0.0	-1.018674	-0.374906	0.601911	0.338096	0.555462	0.882230	0.758085	-1.191138
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.660400	0.325900	0.102053	-0.877232	-0.426230	1.191438	0.0	0.510149	-0.691946	0.601911	0.338096	-0.987512	-0.618722	0.240227	-0.596855

Checking linearity

from statsmodels.stats.outliers_influence import variance_inflation_factor

vif3 = pd.DataFrame()
vif3['Features']=train_merge3.columns
vif3['VIF'] = [variance_inflation_factor(train_merge3.values,i) for i in range(train_merge3.shape[1])]
vif3

	Features	VIF
0	BusinessTravel_Non-Travel	inf
1	BusinessTravel_Travel_Frequently	inf
2	BusinessTravel_Travel_Rarely	inf
3	Department_Human Resources	inf
4	Department_Research & Development	inf
5	Department_Sales	inf
6	EducationField_Human Resources	inf
7	EducationField_Life Sciences	inf
8	EducationField_Marketing	inf
9	EducationField_Medical	inf
10	EducationField_Other	inf
11	EducationField_Technical Degree	inf
12	Gender_Female	inf
13	Gender_Male	inf
14	JobRole_Healthcare Representative	inf
15	JobRole_Human Resources	inf
16	JobRole_Laboratory Technician	inf
17	JobRole_Manager	inf
18	JobRole_Manufacturing Director	inf
19	JobRole_Research Director	inf
20	JobRole_Research Scientist	inf
21	JobRole_Sales Executive	inf
22	JobRole_Sales Representative	inf
23	MaritalStatus_Divorced	inf
24	MaritalStatus_Married	inf
25	MaritalStatus_Single	inf
26	Over18_Y	0.000000
27	OverTime_No	inf
28	OverTime_Yes	inf
29	Attrition	1.347585
30	Age	1.758788
31	DailyRate	1.038182
32	DistanceFromHome	1.038291
33	Education	1.087140
34	EmployeeCount	NaN
35	EmployeeNumber	1.035390
36	EnvironmentSatisfaction	1.060889
37	HourlyRate	1.032229
38	JobInvolvement	1.042954
39	JobLevel	6.346295
40	JobSatisfaction	1.046714
41	MonthlyIncome	2.667871
42	MonthlyRate	1.025837
43	NumCompaniesWorked	1.282556
44	PercentSalaryHike	2.572648
45	PerformanceRating	2.550537
46	RelationshipSatisfaction	1.037069
47	StandardHours	NaN
48	StockOptionLevel	2.185174
49	TotalWorkingYears	2.756983
50	TrainingTimesLastYear	1.030164
51	WorkLifeBalance	1.032552
52	YearsAtCompany	4.548332
53	YearsInCurrentRole	3.380085
54	YearsSinceLastPromotion	1.329397
55	YearsWithCurrManager	2.937798

featurestodrop = vif3.loc[vif3['VIF']>10]
droplist = featurestodrop['Features']
droplist = list(droplist)
len(droplist)
print(droplist)

['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely', 'Department_Human Resources', 'Department_Research & Development', 'Department_Sales', 'EducationField_Human Resources', 'EducationField_Life Sciences', 'EducationField_Marketing', 'EducationField_Medical', 'EducationField_Other', 'EducationField_Technical Degree', 'Gender_Female', 'Gender_Male', 'JobRole_Healthcare Representative', 'JobRole_Human Resources', 'JobRole_Laboratory Technician', 'JobRole_Manager', 'JobRole_Manufacturing Director', 'JobRole_Research Director', 'JobRole_Research Scientist', 'JobRole_Sales Executive', 'JobRole_Sales Representative', 'MaritalStatus_Divorced', 'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_No', 'OverTime_Yes']

drop_list3 = ['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely', 
              'Department_Human Resources', 'Department_Research & Development', 'Department_Sales', 
              'EducationField_Human Resources', 'EducationField_Life Sciences', 'EducationField_Marketing', 
              'EducationField_Medical', 'EducationField_Other', 'EducationField_Technical Degree', 'Gender_Female', 
              'Gender_Male', 'JobRole_Healthcare Representative', 'JobRole_Human Resources', 
              'JobRole_Laboratory Technician', 'JobRole_Manager','JobRole_Manufacturing Director', 
              'JobRole_Research Director','JobRole_Research Scientist', 'JobRole_Sales Executive', 
              'JobRole_Sales Representative', 'MaritalStatus_Divorced', 'MaritalStatus_Married', 'MaritalStatus_Single', 
              'OverTime_No', 'OverTime_Yes']

df_final3 = train_merge3.drop(drop_list3,axis=1)

df_final3.head()

	Over18_Y	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	0.129018	0.726020	2.529583	-1.150554	-0.426230	-1.584178	0.0	-1.018674	-0.374906	0.312607	-2.493820	0.041137	-0.018341	-0.795491	0.294570
1	1	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.140791	1.488876	-0.672478	2.129306	2.346151	1.191438	0.0	0.510149	-0.057867	0.601911	0.338096	1.069787	0.882230	-0.277632	0.888852
2	1	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-1.091220	-1.674841	1.614708	-0.057267	-0.426230	-0.658973	0.0	-1.018674	-0.533426	0.601911	0.338096	-1.501837	-1.219103	-0.795491	-1.191138
3	1	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.835167	1.243211	-0.672478	-1.150554	-0.426230	0.266233	0.0	-1.018674	-0.374906	0.601911	0.338096	0.555462	0.882230	0.758085	-1.191138
4	1	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.660400	0.325900	0.102053	-0.877232	-0.426230	1.191438	0.0	0.510149	-0.691946	0.601911	0.338096	-0.987512	-0.618722	0.240227	-0.596855

Ckeaking Skewness

train_merge3.skew().sort_values()

OverTime_No                         -0.964489
BusinessTravel_Travel_Rarely        -0.923992
Department_Research & Development   -0.646936
WorkLifeBalance                     -0.552480
JobInvolvement                      -0.498419
Gender_Male                         -0.408665
JobSatisfaction                     -0.329672
EnvironmentSatisfaction             -0.321654
RelationshipSatisfaction            -0.302828
Education                           -0.289681
HourlyRate                          -0.032311
DailyRate                           -0.003519
TrainingTimesLastYear               -0.001942
StandardHours                        0.000000
Over18_Y                             0.000000
EmployeeCount                        0.000000
EmployeeNumber                       0.016574
MonthlyRate                          0.018578
MaritalStatus_Married                0.169484
EducationField_Life Sciences         0.356919
Gender_Female                        0.408665
Age                                  0.413286
StockOptionLevel                     0.512145
YearsWithCurrManager                 0.652676
YearsInCurrentRole                   0.686683
YearsAtCompany                       0.733775
MaritalStatus_Single                 0.773874
TotalWorkingYears                    0.779596
EducationField_Medical               0.794118
PercentSalaryHike                    0.821128
Department_Sales                     0.856158
DistanceFromHome                     0.958118
OverTime_Yes                         0.964489
NumCompaniesWorked                   0.987236
JobLevel                             1.025401
MonthlyIncome                        1.159989
MaritalStatus_Divorced               1.336093
JobRole_Sales Executive              1.340834
JobRole_Research Scientist           1.512214
YearsSinceLastPromotion              1.520114
BusinessTravel_Travel_Frequently     1.595067
JobRole_Laboratory Technician        1.701604
Attrition                            1.844366
PerformanceRating                    1.921883
EducationField_Marketing             2.525783
BusinessTravel_Non-Travel            2.632066
JobRole_Manufacturing Director       2.694844
EducationField_Technical Degree      2.872604
JobRole_Healthcare Representative    2.887251
JobRole_Manager                      3.392611
JobRole_Sales Representative         3.847192
EducationField_Other                 3.875119
JobRole_Research Director            3.932443
Department_Human Resources           4.518824
JobRole_Human Resources              5.035637
EducationField_Human Resources       7.181112
dtype: float64

Checking Correlation

train_merge3.corr()['Attrition']

BusinessTravel_Non-Travel           -0.074457
BusinessTravel_Travel_Frequently     0.115143
BusinessTravel_Travel_Rarely        -0.049538
Department_Human Resources           0.016832
Department_Research & Development   -0.085293
Department_Sales                     0.080855
EducationField_Human Resources       0.036466
EducationField_Life Sciences        -0.032703
EducationField_Marketing             0.055781
EducationField_Medical              -0.046999
EducationField_Other                -0.017898
EducationField_Technical Degree      0.069355
Gender_Female                       -0.029453
Gender_Male                          0.029453
JobRole_Healthcare Representative   -0.078696
JobRole_Human Resources              0.036215
JobRole_Laboratory Technician        0.098290
JobRole_Manager                     -0.083316
JobRole_Manufacturing Director      -0.082994
JobRole_Research Director           -0.088870
JobRole_Research Scientist          -0.000360
JobRole_Sales Executive              0.019774
JobRole_Sales Representative         0.157234
MaritalStatus_Divorced              -0.087716
MaritalStatus_Married               -0.090984
MaritalStatus_Single                 0.175419
Over18_Y                                  NaN
OverTime_No                         -0.246118
OverTime_Yes                         0.246118
Attrition                            1.000000
Age                                 -0.159205
DailyRate                           -0.056652
DistanceFromHome                     0.077924
Education                           -0.031373
EmployeeCount                             NaN
EmployeeNumber                      -0.010577
EnvironmentSatisfaction             -0.103369
HourlyRate                          -0.006846
JobInvolvement                      -0.130016
JobLevel                            -0.169105
JobSatisfaction                     -0.103481
MonthlyIncome                       -0.146207
MonthlyRate                          0.015170
NumCompaniesWorked                   0.030383
PercentSalaryHike                   -0.013478
PerformanceRating                    0.002889
RelationshipSatisfaction            -0.045872
StandardHours                             NaN
StockOptionLevel                    -0.186680
TotalWorkingYears                   -0.183018
TrainingTimesLastYear                0.005146
WorkLifeBalance                     -0.063939
YearsAtCompany                      -0.172690
YearsInCurrentRole                  -0.164386
YearsSinceLastPromotion             -0.026458
YearsWithCurrManager                -0.150640
Name: Attrition, dtype: float64

4. Problem Statements

# 4. Explore the data using Exploratory Data Analysis- for pairs of Y and all xs

# Read the data

df_attr4 = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')
df_attr4.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

# EDA

# Seperate Categorical and continious columns

cat = []
con = []
for i in df_attr4.columns:
    if (df_attr4[i].dtypes == 'object'):
        cat.append(i)
    else:
        con.append(i)

df_train_cat4 = cat
df_train_cat4 = df_attr4[cat]
df_train_cat4

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

df_train_con4 = con
df_train_con4 = df_attr4[con]
df_train_con4

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 27 columns

# Visulization of categorical columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train_cat4.columns):
    if df_train_cat4[i].dtypes == 'object':
        plt.subplot(3,3,x1+1)
        sns.countplot(x=df_train_cat4[i])

# Visulisation of continous columns 

plt.figure(figsize=(17,19))
for x1,i in enumerate(df_train_con4.columns):
    if df_train_con4[i].dtypes == 'int64' or df_train_con4[i].dtypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con4[i])

Removing Outliers

for i in df_train_con4.columns:
    q1 = df_train_con4[i].quantile(0.25)
    q2 = df_train_con4[i].quantile(0.75)
    IQR = q3-q1
    uppertail = q3+1.5*IQR
    lowertail = q1-1.5*IQR
    df_train_con4.loc[(df_train_con4[i]>uppertail) | (df_train_con4[i]<lowertail)]
    mean_1 = df_train_con4[i].mean()
    df_train_con4.loc[(df_train_con4[i]>uppertail) | (df_train_con4[i]<lowertail),i]=mean_1

plt.figure(figsize=(17,19))
for x1,i in enumerate(df_train_con4.columns):
    if df_train_con4[i].dtypes == 'int64' or df_train_con4[i].dtypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con4[i])

One hot Encoding

df_dum_train4 = pd.get_dummies(df_train_cat4)

df_dum_train4

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0

1470 rows × 29 columns

Standardization

std_scaler = StandardScaler()
std_scaler1 = std_scaler.fit_transform(df_train_con4)

x4 = pd.DataFrame(std_scaler1,columns=df_train_con4.columns)

x4.head()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	2.280906	7.105427e-15	0.0	-1.412470	-0.891688	0.0	4.547474e-13	-0.660531	-2.842171e-14	0.379672	-0.057788	1.153254	0.0	1.818989e-12	2.125136	3.552714e-15	-0.426230	-1.584178	0.0	-0.932014	-0.905232	-2.171982	-2.493820	0.184086	-0.018341	-0.679146	0.294570
1	-0.438422	7.105427e-15	0.0	0.509209	-1.868426	0.0	4.547474e-13	0.254625	-2.842171e-14	-1.026167	-0.057788	-0.660853	0.0	1.818989e-12	-0.678049	3.552714e-15	2.346151	1.191438	0.0	0.241988	0.587285	0.155707	0.338096	1.440441	0.882230	-0.368715	0.888852
2	2.280906	7.105427e-15	0.0	-1.137945	-0.891688	0.0	4.547474e-13	1.169781	-2.842171e-14	-1.026167	-0.961486	0.246200	0.0	1.818989e-12	1.324226	3.552714e-15	-0.426230	-0.658973	0.0	-0.932014	-1.360324	0.155707	0.338096	-1.700445	-1.219103	-0.679146	-1.191138
3	-0.438422	7.105427e-15	0.0	-0.863419	1.061787	0.0	4.547474e-13	1.169781	-2.842171e-14	0.379672	-0.961486	0.246200	0.0	1.818989e-12	-0.678049	3.552714e-15	-0.426230	0.266233	0.0	-0.932014	-0.905232	0.155707	0.338096	0.812264	0.882230	0.252146	-1.191138
4	-0.438422	7.105427e-15	0.0	-1.137945	-1.868426	0.0	4.547474e-13	-1.575686	-2.842171e-14	0.379672	-0.961486	-0.660853	0.0	1.818989e-12	2.525591	3.552714e-15	-0.426230	1.191438	0.0	0.241988	-1.815417	0.155707	0.338096	-1.072268	-0.618722	-0.058285	-0.596855

df_train_merge4 = pd.concat([df_dum_train4,x4],axis=1)

df_train_merge4.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	2.280906	7.105427e-15	0.0	-1.412470	-0.891688	0.0	4.547474e-13	-0.660531	-2.842171e-14	0.379672	-0.057788	1.153254	0.0	1.818989e-12	2.125136	3.552714e-15	-0.426230	-1.584178	0.0	-0.932014	-0.905232	-2.171982	-2.493820	0.184086	-0.018341	-0.679146	0.294570
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	-0.438422	7.105427e-15	0.0	0.509209	-1.868426	0.0	4.547474e-13	0.254625	-2.842171e-14	-1.026167	-0.057788	-0.660853	0.0	1.818989e-12	-0.678049	3.552714e-15	2.346151	1.191438	0.0	0.241988	0.587285	0.155707	0.338096	1.440441	0.882230	-0.368715	0.888852
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	2.280906	7.105427e-15	0.0	-1.137945	-0.891688	0.0	4.547474e-13	1.169781	-2.842171e-14	-1.026167	-0.961486	0.246200	0.0	1.818989e-12	1.324226	3.552714e-15	-0.426230	-0.658973	0.0	-0.932014	-1.360324	0.155707	0.338096	-1.700445	-1.219103	-0.679146	-1.191138
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	-0.438422	7.105427e-15	0.0	-0.863419	1.061787	0.0	4.547474e-13	1.169781	-2.842171e-14	0.379672	-0.961486	0.246200	0.0	1.818989e-12	-0.678049	3.552714e-15	-0.426230	0.266233	0.0	-0.932014	-0.905232	0.155707	0.338096	0.812264	0.882230	0.252146	-1.191138
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	7.105427e-15	0.0	-1.137945	-1.868426	0.0	4.547474e-13	-1.575686	-2.842171e-14	0.379672	-0.961486	-0.660853	0.0	1.818989e-12	2.525591	3.552714e-15	-0.426230	1.191438	0.0	0.241988	-1.815417	0.155707	0.338096	-1.072268	-0.618722	-0.058285	-0.596855

Checking Correlation

df_train_merge4.corr()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
BusinessTravel_Non-Travel	1.000000	-0.162435	-0.526850	-0.004755	-0.005013	0.007283	0.020835	0.005311	-0.030567	0.012828	-0.013389	0.004171	-0.050461	0.050461	0.012878	-0.015890	0.009270	0.014078	-0.013536	-0.021431	-0.010116	0.031022	-0.033780	0.057455	-0.043635	-0.004622	NaN	0.037163	-0.037163	-0.074457	NaN	NaN	0.008097	0.004524	NaN	NaN	0.003568	NaN	-0.045779	-0.007295	0.019802	NaN	NaN	0.002718	NaN	0.018310	0.021132	NaN	0.028807	0.018476	-0.020746	0.005780	0.023331	0.014365	0.020815	0.029183
BusinessTravel_Travel_Frequently	-0.162435	1.000000	-0.753092	-0.007485	0.003340	-0.000160	0.011818	0.031128	-0.016586	-0.005367	-0.011004	-0.023569	0.022015	-0.022015	0.008029	0.001896	0.010023	-0.042583	0.009783	-0.023579	-0.004461	-0.010175	0.055469	0.005779	-0.030785	0.027734	NaN	-0.029392	0.029392	0.115143	NaN	NaN	-0.022222	-0.008292	NaN	NaN	-0.012624	NaN	0.004424	-0.021557	0.027117	NaN	NaN	-0.039718	NaN	0.016463	0.028500	NaN	-0.016142	0.006153	0.006193	0.010199	0.021100	0.013334	0.023216	0.029774
BusinessTravel_Travel_Rarely	-0.526850	-0.753092	1.000000	0.009618	0.000465	-0.004718	-0.024073	-0.030355	0.034668	-0.003930	0.018406	0.017521	0.014682	-0.014682	-0.015503	0.008962	-0.014815	0.027294	0.000598	0.034600	0.010588	-0.011920	-0.025257	-0.043287	0.055613	-0.020808	NaN	0.000539	-0.000539	-0.049538	NaN	NaN	0.013743	0.004126	NaN	NaN	0.008496	NaN	0.026714	0.023433	-0.036562	NaN	NaN	0.032401	NaN	-0.026390	-0.038640	NaN	-0.005303	-0.017619	0.008498	-0.012640	-0.033732	-0.021064	-0.033877	-0.045106
Department_Human Resources	-0.004755	-0.007485	0.009618	1.000000	-0.290754	-0.139650	0.646436	-0.068040	-0.073692	-0.049761	-0.007527	-0.019469	-0.035652	0.035652	-0.066186	0.904983	-0.097859	0.087615	-0.070000	-0.050765	-0.105352	-0.112959	-0.051764	0.016037	0.034767	-0.051443	NaN	0.006178	-0.006178	0.016832	NaN	NaN	-0.019777	0.011435	NaN	NaN	-0.007597	NaN	0.004789	-0.006157	-0.024068	NaN	NaN	0.020618	NaN	-0.006385	0.034583	NaN	-0.004000	-0.007662	-0.040022	0.047763	-0.005295	-0.033121	-0.026931	-0.021503
Department_Research & Development	-0.005013	0.003340	0.000465	-0.290754	1.000000	-0.906818	-0.187954	0.127321	-0.478520	0.183548	0.064751	0.038541	-0.015760	0.015760	0.227637	-0.263128	0.336570	-0.071356	0.240754	0.174596	0.362340	-0.733497	-0.336127	0.035158	-0.019997	-0.009990	NaN	0.003036	-0.003036	-0.085293	NaN	NaN	-0.026238	-0.018604	NaN	NaN	0.027976	NaN	0.023187	-0.107830	-0.002798	NaN	NaN	0.022237	NaN	0.032720	-0.004587	NaN	0.016927	-0.004442	-0.006819	-0.069922	-0.036307	-0.037461	-0.021497	-0.024626
Department_Sales	0.007283	-0.000160	-0.004718	-0.139650	-0.906818	1.000000	-0.090275	-0.101791	0.527691	-0.168034	-0.063695	-0.031309	0.032017	-0.032017	-0.206425	-0.126381	-0.305208	0.035248	-0.218320	-0.158327	-0.328576	0.808869	0.370667	-0.043451	0.005378	0.033002	NaN	-0.005864	0.005864	0.080855	NaN	NaN	0.035867	0.014215	NaN	NaN	-0.025606	NaN	-0.026107	0.114307	0.013499	NaN	NaN	-0.032097	NaN	-0.031050	-0.010489	NaN	-0.015755	0.007973	0.024688	0.051320	0.039907	0.053360	0.034112	0.034959
EducationField_Human Resources	0.020835	0.011818	-0.024073	0.646436	-0.187954	-0.090275	1.000000	-0.114559	-0.047637	-0.092899	-0.033248	-0.042964	-0.028956	0.028956	-0.042785	0.549751	-0.063260	0.082271	-0.045251	-0.032816	-0.068103	-0.073020	-0.033462	0.012107	0.057339	-0.072051	NaN	-0.004040	0.004040	0.036466	NaN	NaN	-0.005234	0.026479	NaN	NaN	-0.006898	NaN	0.002079	0.010409	-0.021467	NaN	NaN	0.031007	NaN	-0.016167	0.041105	NaN	0.021206	0.004348	-0.037664	-0.003967	-0.008196	-0.020759	-0.023700	-0.025943
EducationField_Life Sciences	0.005311	0.031128	-0.030355	-0.068040	0.127321	-0.101791	-0.114559	1.000000	-0.291660	-0.568774	-0.203560	-0.263050	-0.006770	0.006770	0.029084	-0.063119	0.044359	-0.011143	0.052023	0.018401	0.043729	-0.091122	-0.043208	-0.002672	-0.017866	0.021469	NaN	0.013787	-0.013787	-0.032703	NaN	NaN	-0.045394	0.013184	NaN	NaN	-0.024526	NaN	0.003228	-0.008431	0.052004	NaN	NaN	-0.006131	NaN	0.010853	-0.019973	NaN	-0.017993	0.047855	-0.039018	-0.039728	-0.013111	0.004035	-0.002480	0.003488
EducationField_Marketing	-0.030567	-0.016586	0.034668	-0.073692	-0.478520	0.527691	-0.047637	-0.291660	1.000000	-0.236514	-0.084647	-0.109385	0.024143	-0.024143	-0.108929	-0.066690	-0.161055	0.025577	-0.115206	-0.083548	-0.173387	0.457308	0.133065	-0.007212	0.018491	-0.013323	NaN	-0.014607	0.014607	0.055781	NaN	NaN	0.054723	0.072405	NaN	NaN	0.000479	NaN	-0.018657	0.092698	-0.023528	NaN	NaN	-0.018611	NaN	-0.020918	-0.006580	NaN	0.022560	0.029466	-0.029046	0.018500	0.016770	0.037816	0.006219	0.022774
EducationField_Medical	0.012828	-0.005367	-0.003930	-0.049761	0.183548	-0.168034	-0.092899	-0.568774	-0.236514	1.000000	-0.165072	-0.213314	0.013146	-0.013146	0.034165	-0.042895	0.066262	-0.001128	0.035496	0.062898	0.039735	-0.133532	-0.051990	0.013316	-0.007139	-0.004249	NaN	-0.002246	0.002246	-0.046999	NaN	NaN	0.021864	-0.072335	NaN	NaN	-0.021299	NaN	0.017103	-0.014114	-0.022645	NaN	NaN	0.024826	NaN	0.014868	0.030494	NaN	0.033750	-0.050973	0.070542	0.001641	0.009381	-0.020740	0.022665	-0.005065
EducationField_Other	-0.013389	-0.011004	0.018406	-0.007527	0.064751	-0.063695	-0.033248	-0.203560	-0.084647	-0.165072	1.000000	-0.076343	-0.022992	0.022992	0.017609	0.001594	0.058759	-0.008046	-0.010820	-0.006044	0.005286	-0.036995	-0.033774	0.005411	-0.009171	0.004972	NaN	-0.024970	0.024970	-0.017898	NaN	NaN	0.027156	0.038043	NaN	NaN	0.064602	NaN	-0.011895	-0.016724	0.003380	NaN	NaN	-0.012870	NaN	0.011449	-0.020305	NaN	-0.042100	-0.039133	-0.008151	0.031812	-0.022235	-0.006238	-0.039931	-0.015490
EducationField_Technical Degree	0.004171	-0.023569	0.017521	-0.019469	0.038541	-0.031309	-0.042964	-0.263050	-0.109385	-0.213314	-0.076343	1.000000	-0.003886	0.003886	0.018681	-0.008623	-0.026589	-0.038946	0.007817	-0.022905	0.076218	-0.058843	0.057185	-0.019243	0.002710	0.014265	NaN	0.017723	-0.017723	0.069355	NaN	NaN	-0.036177	-0.026742	NaN	NaN	0.027713	NaN	-0.004519	-0.054707	-0.019795	NaN	NaN	-0.013819	NaN	-0.021729	-0.011044	NaN	-0.024560	-0.002168	0.008289	0.021962	0.010803	0.000445	0.003853	0.002107
Gender_Female	-0.050461	0.022015	0.014682	-0.035652	-0.015760	0.032017	-0.028956	-0.006770	0.024143	0.013146	-0.022992	-0.003886	1.000000	-1.000000	-0.006823	-0.036082	-0.067793	0.033880	0.065197	0.006121	-0.009745	0.005348	0.028877	-0.046076	0.007804	0.032752	NaN	-0.041924	0.041924	-0.029453	NaN	NaN	-0.003885	0.016547	NaN	NaN	-0.000508	NaN	-0.017960	0.039403	-0.033252	NaN	NaN	0.039147	NaN	0.013859	-0.022868	NaN	-0.012716	0.040533	0.038787	0.002753	0.026297	0.032762	0.026985	0.034464
Gender_Male	0.050461	-0.022015	-0.014682	0.035652	0.015760	-0.032017	0.028956	0.006770	-0.024143	-0.013146	0.022992	0.003886	-1.000000	1.000000	0.006823	0.036082	0.067793	-0.033880	-0.065197	-0.006121	0.009745	-0.005348	-0.028877	0.046076	-0.007804	-0.032752	NaN	0.041924	-0.041924	0.029453	NaN	NaN	0.003885	-0.016547	NaN	NaN	0.000508	NaN	0.017960	-0.039403	0.033252	NaN	NaN	-0.039147	NaN	-0.013859	0.022868	NaN	0.012716	-0.040533	-0.038787	-0.002753	-0.026297	-0.032762	-0.026985	-0.034464
JobRole_Healthcare Representative	0.012878	0.008029	-0.015503	-0.066186	0.227637	-0.206425	-0.042785	0.029084	-0.108929	0.034165	0.017609	0.018681	-0.006823	0.006823	1.000000	-0.059898	-0.144652	-0.085409	-0.103472	-0.075038	-0.155727	-0.166971	-0.076515	0.027897	0.004913	-0.030126	NaN	0.000382	-0.000382	-0.078696	NaN	NaN	0.024199	0.024270	NaN	NaN	0.014090	NaN	0.001272	0.115704	0.016367	NaN	NaN	0.026955	NaN	-0.000928	-0.005090	NaN	0.014021	0.102770	-0.012432	-0.026101	0.080206	0.067537	0.075902	0.043270
JobRole_Human Resources	-0.015890	0.001896	0.008962	0.904983	-0.263128	-0.126381	0.549751	-0.063119	-0.066690	-0.042895	0.001594	-0.008623	-0.036082	0.036082	-0.059898	1.000000	-0.088561	-0.052290	-0.063349	-0.045941	-0.095342	-0.102226	-0.046845	0.021541	0.030995	-0.052320	NaN	0.014026	-0.014026	0.036215	NaN	NaN	-0.026719	-0.005295	NaN	NaN	-0.022014	NaN	-0.004952	-0.100922	-0.029681	NaN	NaN	0.020578	NaN	-0.010154	0.044169	NaN	-0.009864	-0.032191	-0.035902	0.043887	-0.020259	-0.053260	-0.054603	-0.047546
JobRole_Laboratory Technician	0.009270	0.010023	-0.014815	-0.097859	0.336570	-0.305208	-0.063260	0.044359	-0.161055	0.066262	0.058759	-0.026589	-0.067793	0.067793	-0.144652	-0.088561	1.000000	-0.126280	-0.152987	-0.110947	-0.230248	-0.246873	-0.113130	-0.011224	-0.009233	0.019873	NaN	0.044774	-0.044774	0.098290	NaN	NaN	0.014273	-0.063566	NaN	NaN	-0.001533	NaN	-0.022724	-0.344608	-0.015710	NaN	NaN	-0.021121	NaN	0.010796	-0.010691	NaN	0.013386	-0.071452	0.053998	-0.028209	-0.125688	-0.119437	-0.110099	-0.117188
JobRole_Manager	0.014078	-0.042583	0.027294	0.087615	-0.071356	0.035248	0.082271	-0.011143	0.025577	-0.001128	-0.008046	-0.038946	0.033880	-0.033880	-0.085409	-0.052290	-0.126280	1.000000	-0.090330	-0.065508	-0.135949	-0.145765	-0.066797	0.001997	0.049982	-0.055176	NaN	0.011086	-0.011086	-0.083316	NaN	NaN	-0.066843	0.028453	NaN	NaN	0.010730	NaN	0.017112	0.552744	-0.005620	NaN	NaN	0.042125	NaN	0.032050	0.025638	NaN	-0.015637	0.160364	0.003052	0.005137	0.076676	0.137535	0.224255	0.146055
JobRole_Manufacturing Director	-0.013536	0.009783	0.000598	-0.070000	0.240754	-0.218320	-0.045251	0.052023	-0.115206	0.035496	-0.010820	0.007817	0.065197	-0.065197	-0.103472	-0.063349	-0.152987	-0.090330	1.000000	-0.079362	-0.164700	-0.176592	-0.080924	0.020543	0.002819	-0.021331	NaN	0.010302	-0.010302	-0.082994	NaN	NaN	-0.019004	-0.005290	NaN	NaN	0.059178	NaN	-0.021939	0.114896	-0.013747	NaN	NaN	0.009580	NaN	0.029775	0.003640	NaN	0.007735	0.033861	-0.013987	0.002011	0.087510	0.075061	-0.007241	0.084649
JobRole_Research Director	-0.021431	-0.023579	0.034600	-0.050765	0.174596	-0.158327	-0.032816	0.018401	-0.083548	0.062898	-0.006044	-0.022905	0.006121	-0.006121	-0.075038	-0.045941	-0.110947	-0.065508	-0.079362	1.000000	-0.119442	-0.128066	-0.058687	0.037524	0.008271	-0.042299	NaN	-0.002400	0.002400	-0.088870	NaN	NaN	-0.003730	0.049694	NaN	NaN	-0.048689	NaN	0.015200	0.414319	-0.006217	NaN	NaN	0.097925	NaN	-0.035744	-0.005492	NaN	0.015807	0.140892	-0.004527	0.034403	0.078271	0.075569	0.074455	0.116442
JobRole_Research Scientist	-0.010116	-0.004461	0.010588	-0.105352	0.362340	-0.328576	-0.068103	0.043729	-0.173387	0.039735	0.005286	0.076218	-0.009745	0.009745	-0.155727	-0.095342	-0.230248	-0.135949	-0.164700	-0.119442	1.000000	-0.265775	-0.121792	-0.012115	-0.039987	0.053522	NaN	-0.054378	0.054378	-0.000360	NaN	NaN	-0.021396	0.000709	NaN	NaN	0.001940	NaN	0.047604	-0.387788	0.020503	NaN	NaN	-0.043981	NaN	0.019416	-0.003116	NaN	-0.011635	-0.169943	-0.052126	-0.058613	-0.115012	-0.123154	-0.105237	-0.124838
JobRole_Sales Executive	0.031022	-0.010175	-0.011920	-0.112959	-0.733497	0.808869	-0.073020	-0.091122	0.457308	-0.133532	-0.036995	-0.058843	0.005348	-0.005348	-0.166971	-0.102226	-0.246873	-0.145765	-0.176592	-0.128066	-0.265775	1.000000	-0.130586	-0.013853	0.005751	0.006210	NaN	-0.006341	0.006341	0.019774	NaN	NaN	0.055332	0.053398	NaN	NaN	-0.024421	NaN	-0.011413	0.127490	0.012604	NaN	NaN	0.005913	NaN	-0.041401	-0.004836	NaN	0.015756	-0.050006	0.013241	0.032092	0.133917	0.109504	0.049202	0.099893
JobRole_Sales Representative	-0.033780	0.055469	-0.025257	-0.051764	-0.336127	0.370667	-0.033462	-0.043208	0.133065	-0.051990	-0.033774	0.057185	0.028877	-0.028877	-0.076515	-0.046845	-0.113130	-0.066797	-0.080924	-0.058687	-0.121792	-0.130586	1.000000	-0.052890	-0.023659	0.072439	NaN	-0.003347	0.003347	0.157234	NaN	NaN	0.007154	-0.091465	NaN	NaN	0.002949	NaN	-0.027282	-0.216559	0.001413	NaN	NaN	-0.104494	NaN	-0.006214	-0.024859	NaN	-0.048067	0.041842	0.040377	0.045148	-0.191950	-0.150470	-0.085622	-0.170527
MaritalStatus_Divorced	0.057455	0.005779	-0.043287	0.016037	0.035158	-0.043451	0.012107	-0.002672	-0.007212	0.013316	0.005411	-0.019243	-0.046076	0.046076	0.027897	0.021541	-0.011224	0.001997	0.020543	0.037524	-0.012115	-0.013853	-0.052890	1.000000	-0.491506	-0.366691	NaN	-0.023462	0.023462	-0.087716	NaN	NaN	0.025673	-0.002439	NaN	NaN	0.016439	NaN	0.016815	0.037087	-0.015197	NaN	NaN	0.040824	NaN	-0.010310	0.006199	NaN	0.446285	0.014843	0.008405	-0.009080	0.011309	0.023047	-0.005279	0.015815
MaritalStatus_Married	-0.043635	-0.030785	0.055613	0.034767	-0.019997	0.005378	0.057339	-0.017866	0.018491	-0.007139	-0.009171	0.002710	0.007804	-0.007804	0.004913	0.030995	-0.009233	0.049982	0.002819	0.008271	-0.039987	0.005751	-0.023659	-0.491506	1.000000	-0.629981	NaN	0.013502	-0.013502	-0.090984	NaN	NaN	0.002933	-0.001865	NaN	NaN	-0.022180	NaN	0.028324	0.050547	-0.010315	NaN	NaN	-0.016142	NaN	0.009585	-0.043382	NaN	0.225574	-0.005217	-0.029602	-0.006388	0.066529	0.055687	0.054102	0.036885
MaritalStatus_Single	-0.004622	0.027734	-0.020808	-0.051443	-0.009990	0.033002	-0.072051	0.021469	-0.013323	-0.004249	0.004972	0.014265	0.032752	-0.032752	-0.030126	-0.052320	0.019873	-0.055176	-0.021331	-0.042299	0.053522	0.006210	0.072439	-0.366691	-0.629981	1.000000	NaN	0.006498	-0.006498	0.175419	NaN	NaN	-0.026027	0.004168	NaN	NaN	0.009035	NaN	-0.045253	-0.087072	0.024571	NaN	NaN	-0.019161	NaN	-0.001045	0.040817	NaN	-0.638957	-0.007663	0.024129	0.014921	-0.081157	-0.080043	-0.053090	-0.053507
Over18_Y	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
OverTime_No	0.037163	-0.029392	0.000539	0.006178	0.003036	-0.005864	-0.004040	0.013787	-0.014607	-0.002246	-0.024970	0.017723	-0.041924	0.041924	0.000382	0.014026	0.044774	0.011086	0.010302	-0.002400	-0.054378	-0.006341	-0.003347	-0.023462	0.013502	0.006498	NaN	1.000000	-1.000000	-0.246118	NaN	NaN	-0.038315	0.020322	NaN	NaN	-0.070132	NaN	0.003507	-0.000544	-0.024539	NaN	NaN	0.020786	NaN	-0.004369	-0.048493	NaN	0.000449	-0.003604	0.079113	0.027092	0.048276	0.010861	0.012239	0.033366
OverTime_Yes	-0.037163	0.029392	-0.000539	-0.006178	-0.003036	0.005864	0.004040	-0.013787	0.014607	0.002246	0.024970	-0.017723	0.041924	-0.041924	-0.000382	-0.014026	-0.044774	-0.011086	-0.010302	0.002400	0.054378	0.006341	0.003347	0.023462	-0.013502	-0.006498	NaN	-1.000000	1.000000	0.246118	NaN	NaN	0.038315	-0.020322	NaN	NaN	0.070132	NaN	-0.003507	0.000544	0.024539	NaN	NaN	-0.020786	NaN	0.004369	0.048493	NaN	-0.000449	0.003604	-0.079113	-0.027092	-0.048276	-0.010861	-0.012239	-0.033366
Attrition	-0.074457	0.115143	-0.049538	0.016832	-0.085293	0.080855	0.036466	-0.032703	0.055781	-0.046999	-0.017898	0.069355	-0.029453	0.029453	-0.078696	0.036215	0.098290	-0.083316	-0.082994	-0.088870	-0.000360	0.019774	0.157234	-0.087716	-0.090984	0.175419	NaN	-0.246118	0.246118	1.000000	NaN	NaN	0.073971	-0.031373	NaN	NaN	-0.103369	NaN	-0.130016	-0.169105	-0.103481	NaN	NaN	0.043494	NaN	0.002889	-0.045872	NaN	-0.137145	-0.033917	-0.059478	-0.063939	-0.182422	-0.164386	-0.033019	-0.150640
Age	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
DailyRate	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
DistanceFromHome	0.008097	-0.022222	0.013743	-0.019777	-0.026238	0.035867	-0.005234	-0.045394	0.054723	0.021864	0.027156	-0.036177	-0.003885	0.003885	0.024199	-0.026719	0.014273	-0.066843	-0.019004	-0.003730	-0.021396	0.055332	0.007154	0.025673	0.002933	-0.026027	NaN	-0.038315	0.038315	0.073971	NaN	NaN	1.000000	0.030019	NaN	NaN	-0.001822	NaN	0.048667	-0.014518	-0.028497	NaN	NaN	-0.011595	NaN	-0.000746	0.005234	NaN	0.021909	-0.003448	-0.006813	-0.023779	0.014902	0.005106	-0.013109	0.002100
Education	0.004524	-0.008292	0.004126	0.011435	-0.018604	0.014215	0.026479	0.013184	0.072405	-0.072335	0.038043	-0.026742	0.016547	-0.016547	0.024270	-0.005295	-0.063566	0.028453	-0.005290	0.049694	0.000709	0.053398	-0.091465	-0.002439	-0.001865	0.004168	NaN	0.020322	-0.020322	-0.031373	NaN	NaN	0.030019	1.000000	NaN	NaN	-0.027128	NaN	0.042438	0.101589	-0.011296	NaN	NaN	0.126317	NaN	-0.024539	-0.009118	NaN	0.018422	0.062663	-0.025100	0.009819	0.047600	0.056703	0.054254	0.055449
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EnvironmentSatisfaction	0.003568	-0.012624	0.008496	-0.007597	0.027976	-0.025606	-0.006898	-0.024526	0.000479	-0.021299	0.064602	0.027713	-0.000508	0.000508	0.014090	-0.022014	-0.001533	0.010730	0.059178	-0.048689	0.001940	-0.024421	0.002949	0.016439	-0.022180	0.009035	NaN	-0.070132	0.070132	-0.103369	NaN	NaN	-0.001822	-0.027128	NaN	NaN	1.000000	NaN	-0.008278	0.001212	-0.006784	NaN	NaN	0.012594	NaN	-0.029548	0.007665	NaN	0.003432	0.024419	-0.019359	0.027627	0.020692	0.040470	0.016194	-0.007923
HourlyRate	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
JobInvolvement	-0.045779	0.004424	0.026714	0.004789	0.023187	-0.026107	0.002079	0.003228	-0.018657	0.017103	-0.011895	-0.004519	-0.017960	0.017960	0.001272	-0.004952	-0.022724	0.017112	-0.021939	0.015200	0.047604	-0.011413	-0.027282	0.016815	0.028324	-0.045253	NaN	0.003507	-0.003507	-0.130016	NaN	NaN	0.048667	0.042438	NaN	NaN	-0.008278	NaN	1.000000	-0.012630	-0.021476	NaN	NaN	0.015012	NaN	-0.029071	0.034297	NaN	0.021523	-0.017304	-0.015338	-0.014617	0.033586	0.004117	-0.024184	0.025310
JobLevel	-0.007295	-0.021557	0.023433	-0.006157	-0.107830	0.114307	0.010409	-0.008431	0.092698	-0.014114	-0.016724	-0.054707	0.039403	-0.039403	0.115704	-0.100922	-0.344608	0.552744	0.114896	0.414319	-0.387788	0.127490	-0.216559	0.037087	0.050547	-0.087072	NaN	-0.000544	0.000544	-0.169105	NaN	NaN	-0.014518	0.101589	NaN	NaN	0.001212	NaN	-0.012630	1.000000	-0.001944	NaN	NaN	0.142501	NaN	-0.021222	0.021642	NaN	0.013984	0.356467	-0.018191	0.037818	0.306263	0.332018	0.353885	0.361762
JobSatisfaction	0.019802	0.027117	-0.036562	-0.024068	-0.002798	0.013499	-0.021467	0.052004	-0.023528	-0.022645	0.003380	-0.019795	-0.033252	0.033252	0.016367	-0.029681	-0.015710	-0.005620	-0.013747	-0.006217	0.020503	0.012604	0.001413	-0.015197	-0.010315	0.024571	NaN	-0.024539	0.024539	-0.103481	NaN	NaN	-0.028497	-0.011296	NaN	NaN	-0.006784	NaN	-0.021476	-0.001944	1.000000	NaN	NaN	-0.055699	NaN	0.002297	-0.012454	NaN	0.010690	0.002016	-0.005779	-0.019459	0.018744	0.007938	-0.018214	-0.019899
MonthlyIncome	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
MonthlyRate	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
NumCompaniesWorked	0.002718	-0.039718	0.032401	0.020618	0.022237	-0.032097	0.031007	-0.006131	-0.018611	0.024826	-0.012870	-0.013819	0.039147	-0.039147	0.026955	0.020578	-0.021121	0.042125	0.009580	0.097925	-0.043981	0.005913	-0.104494	0.040824	-0.016142	-0.019161	NaN	0.020786	-0.020786	0.043494	NaN	NaN	-0.011595	0.126317	NaN	NaN	0.012594	NaN	0.015012	0.142501	-0.055699	NaN	NaN	1.000000	NaN	-0.014095	0.052733	NaN	0.030075	0.042451	-0.066054	-0.008366	-0.142141	-0.111122	-0.036814	-0.120770
PercentSalaryHike	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
PerformanceRating	0.018310	0.016463	-0.026390	-0.006385	0.032720	-0.031050	-0.016167	0.010853	-0.020918	0.014868	0.011449	-0.021729	0.013859	-0.013859	-0.000928	-0.010154	0.010796	0.032050	0.029775	-0.035744	0.019416	-0.041401	-0.006214	-0.010310	0.009585	-0.001045	NaN	-0.004369	0.004369	0.002889	NaN	NaN	-0.000746	-0.024539	NaN	NaN	-0.029548	NaN	-0.029071	-0.021222	0.002297	NaN	NaN	-0.014095	NaN	1.000000	-0.031351	NaN	0.003506	0.011048	-0.015579	0.002572	0.029423	0.034031	0.017896	0.025381
RelationshipSatisfaction	0.021132	0.028500	-0.038640	0.034583	-0.004587	-0.010489	0.041105	-0.019973	-0.006580	0.030494	-0.020305	-0.011044	-0.022868	0.022868	-0.005090	0.044169	-0.010691	0.025638	0.003640	-0.005492	-0.003116	-0.004836	-0.024859	0.006199	-0.043382	0.040817	NaN	-0.048493	0.048493	-0.045872	NaN	NaN	0.005234	-0.009118	NaN	NaN	0.007665	NaN	0.034297	0.021642	-0.012454	NaN	NaN	0.052733	NaN	-0.031351	1.000000	NaN	-0.045952	-0.004342	0.002497	0.019604	-0.021786	-0.021509	0.033493	-0.005533
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	0.028807	-0.016142	-0.005303	-0.004000	0.016927	-0.015755	0.021206	-0.017993	0.022560	0.033750	-0.042100	-0.024560	-0.012716	0.012716	0.014021	-0.009864	0.013386	-0.015637	0.007735	0.015807	-0.011635	0.015756	-0.048067	0.446285	0.225574	-0.638957	NaN	0.000449	-0.000449	-0.137145	NaN	NaN	0.021909	0.018422	NaN	NaN	0.003432	NaN	0.021523	0.013984	0.010690	NaN	NaN	0.030075	NaN	0.003506	-0.045952	NaN	1.000000	0.016672	0.011274	0.004129	0.057714	0.062072	0.014352	0.037675
TotalWorkingYears	0.018476	0.006153	-0.017619	-0.007662	-0.004442	0.007973	0.004348	0.047855	0.029466	-0.050973	-0.039133	-0.002168	0.040533	-0.040533	0.102770	-0.032191	-0.071452	0.160364	0.033861	0.140892	-0.169943	-0.050006	0.041842	0.014843	-0.005217	-0.007663	NaN	-0.003604	0.003604	-0.033917	NaN	NaN	-0.003448	0.062663	NaN	NaN	0.024419	NaN	-0.017304	0.356467	0.002016	NaN	NaN	0.042451	NaN	0.011048	-0.004342	NaN	0.016672	1.000000	0.009694	0.003894	0.198983	0.228646	0.216931	0.244631
TrainingTimesLastYear	-0.020746	0.006193	0.008498	-0.040022	-0.006819	0.024688	-0.037664	-0.039018	-0.029046	0.070542	-0.008151	0.008289	0.038787	-0.038787	-0.012432	-0.035902	0.053998	0.003052	-0.013987	-0.004527	-0.052126	0.013241	0.040377	0.008405	-0.029602	0.024129	NaN	0.079113	-0.079113	-0.059478	NaN	NaN	-0.006813	-0.025100	NaN	NaN	-0.019359	NaN	-0.015338	-0.018191	-0.005779	NaN	NaN	-0.066054	NaN	-0.015579	0.002497	NaN	0.011274	0.009694	1.000000	0.028072	0.000134	-0.001871	-0.002067	-0.003578
WorkLifeBalance	0.005780	0.010199	-0.012640	0.047763	-0.069922	0.051320	-0.003967	-0.039728	0.018500	0.001641	0.031812	0.021962	0.002753	-0.002753	-0.026101	0.043887	-0.028209	0.005137	0.002011	0.034403	-0.058613	0.032092	0.045148	-0.009080	-0.006388	0.014921	NaN	0.027092	-0.027092	-0.063939	NaN	NaN	-0.023779	0.009819	NaN	NaN	0.027627	NaN	-0.014617	0.037818	-0.019459	NaN	NaN	-0.008366	NaN	0.002572	0.019604	NaN	0.004129	0.003894	0.028072	1.000000	0.006952	0.036562	0.008941	-0.005749
YearsAtCompany	0.023331	0.021100	-0.033732	-0.005295	-0.036307	0.039907	-0.008196	-0.013111	0.016770	0.009381	-0.022235	0.010803	0.026297	-0.026297	0.080206	-0.020259	-0.125688	0.076676	0.087510	0.078271	-0.115012	0.133917	-0.191950	0.011309	0.066529	-0.081157	NaN	0.048276	-0.048276	-0.182422	NaN	NaN	0.014902	0.047600	NaN	NaN	0.020692	NaN	0.033586	0.306263	0.018744	NaN	NaN	-0.142141	NaN	0.029423	-0.021786	NaN	0.057714	0.198983	0.000134	0.006952	1.000000	0.771709	0.421157	0.745037
YearsInCurrentRole	0.014365	0.013334	-0.021064	-0.033121	-0.037461	0.053360	-0.020759	0.004035	0.037816	-0.020740	-0.006238	0.000445	0.032762	-0.032762	0.067537	-0.053260	-0.119437	0.137535	0.075061	0.075569	-0.123154	0.109504	-0.150470	0.023047	0.055687	-0.080043	NaN	0.010861	-0.010861	-0.164386	NaN	NaN	0.005106	0.056703	NaN	NaN	0.040470	NaN	0.004117	0.332018	0.007938	NaN	NaN	-0.111122	NaN	0.034031	-0.021509	NaN	0.062072	0.228646	-0.001871	0.036562	0.771709	1.000000	0.501135	0.705200
YearsSinceLastPromotion	0.020815	0.023216	-0.033877	-0.026931	-0.021497	0.034112	-0.023700	-0.002480	0.006219	0.022665	-0.039931	0.003853	0.026985	-0.026985	0.075902	-0.054603	-0.110099	0.224255	-0.007241	0.074455	-0.105237	0.049202	-0.085622	-0.005279	0.054102	-0.053090	NaN	0.012239	-0.012239	-0.033019	NaN	NaN	-0.013109	0.054254	NaN	NaN	0.016194	NaN	-0.024184	0.353885	-0.018214	NaN	NaN	-0.036814	NaN	0.017896	0.033493	NaN	0.014352	0.216931	-0.002067	0.008941	0.421157	0.501135	1.000000	0.479890
YearsWithCurrManager	0.029183	0.029774	-0.045106	-0.021503	-0.024626	0.034959	-0.025943	0.003488	0.022774	-0.005065	-0.015490	0.002107	0.034464	-0.034464	0.043270	-0.047546	-0.117188	0.146055	0.084649	0.116442	-0.124838	0.099893	-0.170527	0.015815	0.036885	-0.053507	NaN	0.033366	-0.033366	-0.150640	NaN	NaN	0.002100	0.055449	NaN	NaN	-0.007923	NaN	0.025310	0.361762	-0.019899	NaN	NaN	-0.120770	NaN	0.025381	-0.005533	NaN	0.037675	0.244631	-0.003578	-0.005749	0.745037	0.705200	0.479890	1.000000

df_train_merge4.describe()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1.470000e+03	1.470000e+03	1470.0	1.470000e+03	1.470000e+03	1470.0	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1470.0	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1470.0	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03	1.470000e+03
mean	0.102041	0.188435	0.709524	0.042857	0.653741	0.303401	0.018367	0.412245	0.108163	0.315646	0.055782	0.089796	0.400000	0.600000	0.089116	0.035374	0.176190	0.069388	0.098639	0.054422	0.198639	0.221769	0.056463	0.222449	0.457823	0.319728	1.0	0.717007	0.282993	-7.106938e-17	7.105427e-15	0.0	-1.970457e-16	2.697011e-16	0.0	4.547474e-13	7.054070e-17	-2.842171e-14	-6.495182e-18	1.479542e-16	-7.816876e-18	0.0	1.818989e-12	-5.588878e-17	3.552714e-15	-7.766274e-16	-1.910792e-16	0.0	6.600918e-17	1.253721e-17	-1.106069e-16	-5.165936e-17	-1.540340e-16	-8.851574e-17	-4.236974e-17	-6.993650e-17
std	0.302805	0.391193	0.454137	0.202604	0.475939	0.459884	0.134321	0.492406	0.310692	0.464931	0.229579	0.285986	0.490065	0.490065	0.285008	0.184786	0.381112	0.254199	0.298279	0.226925	0.399112	0.415578	0.230891	0.416033	0.498387	0.466530	0.0	0.450606	0.450606	1.000340e+00	0.000000e+00	0.0	1.000340e+00	1.000340e+00	0.0	0.000000e+00	1.000340e+00	0.000000e+00	1.000340e+00	1.000340e+00	1.000340e+00	0.0	0.000000e+00	1.000340e+00	0.000000e+00	1.000340e+00	1.000340e+00	0.0	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00	1.000340e+00
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.0	0.000000	0.000000	-4.384223e-01	7.105427e-15	0.0	-1.412470e+00	-1.868426e+00	0.0	4.547474e-13	-1.575686e+00	-2.842171e-14	-2.432006e+00	-9.614864e-01	-1.567907e+00	0.0	1.818989e-12	-1.078504e+00	3.552714e-15	-4.262300e-01	-1.584178e+00	0.0	-9.320144e-01	-2.270509e+00	-2.171982e+00	-2.493820e+00	-1.700445e+00	-1.219103e+00	-6.791457e-01	-1.191138e+00
25%	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.0	0.000000	0.000000	-4.384223e-01	7.105427e-15	0.0	-1.137945e+00	-8.916883e-01	0.0	4.547474e-13	-6.605307e-01	-2.842171e-14	-1.026167e+00	-9.614864e-01	-6.608532e-01	0.0	1.818989e-12	-6.780494e-01	3.552714e-15	-4.262300e-01	-6.589728e-01	0.0	-9.320144e-01	-9.052321e-01	-6.201892e-01	-1.077862e+00	-7.581791e-01	-6.187220e-01	-6.791457e-01	-5.968549e-01
50%	0.000000	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.0	1.000000	0.000000	-4.384223e-01	7.105427e-15	0.0	2.346832e-01	8.504925e-02	0.0	4.547474e-13	2.546249e-01	-2.842171e-14	3.796721e-01	-5.778755e-02	2.462002e-01	0.0	1.818989e-12	-2.775943e-01	3.552714e-15	-4.262300e-01	2.662326e-01	0.0	2.419883e-01	5.872853e-01	1.557071e-01	3.380962e-01	-1.300020e-01	-3.185315e-01	-3.687153e-01	-2.997134e-01
75%	0.000000	0.000000	1.000000	0.000000	1.000000	1.000000	0.000000	1.000000	0.000000	1.000000	0.000000	0.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	1.0	1.000000	1.000000	-4.384223e-01	7.105427e-15	0.0	8.365853e-01	1.061787e+00	0.0	4.547474e-13	1.169781e+00	-2.842171e-14	3.796721e-01	8.459113e-01	1.153254e+00	0.0	1.818989e-12	5.233157e-01	3.552714e-15	-4.262300e-01	1.191438e+00	0.0	2.419883e-01	5.872853e-01	1.557071e-01	3.380962e-01	5.007390e-01	8.822302e-01	2.521455e-01	8.888524e-01
max	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.0	1.000000	1.000000	2.280906e+00	7.105427e-15	0.0	2.156362e+00	2.038524e+00	0.0	4.547474e-13	1.169781e+00	-2.842171e-14	1.785511e+00	2.653309e+00	1.153254e+00	0.0	1.818989e-12	2.525591e+00	3.552714e-15	2.346151e+00	1.191438e+00	0.0	2.589994e+00	5.872853e-01	2.483396e+00	1.754054e+00	2.382706e+00	2.983563e+00	3.977310e+00	2.968843e+00

5. Problem Statements

# 5. Visualize all the distribution relationship

# 1. Seperate categorical columns

cat = []
con = []
for i in df_attr.columns:
    if (df_attr[i].dtypes == 'object'):
        cat.append(i)
    else:
        con.append(i)

df_train5_cat1 = cat
df_train5_cat1 = df_attr[cat]
df_train5_cat1 

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime	AGE_GROUP
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes	GROUP2
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No	GROUP3
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes	GROUP2
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes	GROUP2
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	GROUP1
...	...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	GROUP2
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No	GROUP2
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes	GROUP1
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No	GROUP3
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No	GROUP2

1470 rows × 9 columns

df_train5_con1 = con
df_train5_con1 = df_attr[con]
df_train5_con1

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	2	4	5993	19479	8	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	2	2	5130	24907	1	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	1	3	2090	2396	6	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	1	3	2909	23159	1	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	1	2	3468	16632	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	2	4	2571	12290	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	3	1	9991	21457	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	2	2	6142	5174	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	2	2	5390	13243	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	2	3	4404	10228	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 27 columns

# Visulasation of categorical columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train5_cat1.columns):
    if df_train5_cat1[i].dtypes=='object':
        plt.subplot(3,3,x1+1)
        sns.countplot(x=df_train5_cat1[i])

# Visulisation of continious columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train5_con1.columns):
    if df_train5_con1[i].dtypes == 'int64' or df_train5_con1[i].dypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.distplot(df_train5_con1[i])

Checking Skewness

df_attr.skew().sort_values()

WorkLifeBalance            -0.552480
JobInvolvement             -0.498419
JobSatisfaction            -0.329672
EnvironmentSatisfaction    -0.321654
RelationshipSatisfaction   -0.302828
Education                  -0.289681
HourlyRate                 -0.032311
DailyRate                  -0.003519
EmployeeCount               0.000000
StandardHours               0.000000
EmployeeNumber              0.016574
MonthlyRate                 0.018578
Age                         0.413286
TrainingTimesLastYear       0.553124
PercentSalaryHike           0.821128
YearsWithCurrManager        0.833451
YearsInCurrentRole          0.917363
DistanceFromHome            0.958118
StockOptionLevel            0.968980
JobLevel                    1.025401
NumCompaniesWorked          1.026471
TotalWorkingYears           1.117172
MonthlyIncome               1.369817
YearsAtCompany              1.764529
Attrition                   1.844366
PerformanceRating           1.921883
YearsSinceLastPromotion     1.984290
dtype: float64

Checking Correlation

df_attr.corr()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	MonthlyRate	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
Attrition	1.000000	-0.159205	-0.056652	0.077924	-0.031373	NaN	-0.010577	-0.103369	-0.006846	-0.130016	-0.169105	-0.103481	-0.159840	0.015170	0.043494	-0.013478	0.002889	-0.045872	NaN	-0.137145	-0.171063	-0.059478	-0.063939	-0.134392	-0.160545	-0.033019	-0.156199
Age	-0.159205	1.000000	0.010661	-0.001686	0.208034	NaN	-0.010145	0.010146	0.024287	0.029820	0.509604	-0.004892	0.497855	0.028051	0.299635	0.003634	0.001904	0.053535	NaN	0.037510	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089
DailyRate	-0.056652	0.010661	1.000000	-0.004985	-0.016806	NaN	-0.050990	0.018355	0.023381	0.046135	0.002966	0.030571	0.007707	-0.032182	0.038153	0.022704	0.000473	0.007846	NaN	0.042143	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363
DistanceFromHome	0.077924	-0.001686	-0.004985	1.000000	0.021042	NaN	0.032916	-0.016075	0.031131	0.008783	0.005303	-0.003669	-0.017014	0.027473	-0.029251	0.040235	0.027110	0.006557	NaN	0.044872	0.004628	-0.036942	-0.026556	0.009508	0.018845	0.010029	0.014406
Education	-0.031373	0.208034	-0.016806	0.021042	1.000000	NaN	0.042070	-0.027128	0.016775	0.042438	0.101589	-0.011296	0.094961	-0.026084	0.126317	-0.011111	-0.024539	-0.009118	NaN	0.018422	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	-0.010577	-0.010145	-0.050990	0.032916	0.042070	NaN	1.000000	0.017621	0.035179	-0.006888	-0.018519	-0.046247	-0.014829	0.012648	-0.001251	-0.012944	-0.020359	-0.069861	NaN	0.062227	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197
EnvironmentSatisfaction	-0.103369	0.010146	0.018355	-0.016075	-0.027128	NaN	0.017621	1.000000	-0.049857	-0.008278	0.001212	-0.006784	-0.006259	0.037600	0.012594	-0.031701	-0.029548	0.007665	NaN	0.003432	-0.002693	-0.019359	0.027627	0.001458	0.018007	0.016194	-0.004999
HourlyRate	-0.006846	0.024287	0.023381	0.031131	0.016775	NaN	0.035179	-0.049857	1.000000	0.042861	-0.027853	-0.071335	-0.015794	-0.015297	0.022157	-0.009062	-0.002172	0.001330	NaN	0.050263	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123
JobInvolvement	-0.130016	0.029820	0.046135	0.008783	0.042438	NaN	-0.006888	-0.008278	0.042861	1.000000	-0.012630	-0.021476	-0.015271	-0.016322	0.015012	-0.017205	-0.029071	0.034297	NaN	0.021523	-0.005533	-0.015338	-0.014617	-0.021355	0.008717	-0.024184	0.025976
JobLevel	-0.169105	0.509604	0.002966	0.005303	0.101589	NaN	-0.018519	0.001212	-0.027853	-0.012630	1.000000	-0.001944	0.950300	0.039563	0.142501	-0.034730	-0.021222	0.021642	NaN	0.013984	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281
JobSatisfaction	-0.103481	-0.004892	0.030571	-0.003669	-0.011296	NaN	-0.046247	-0.006784	-0.071335	-0.021476	-0.001944	1.000000	-0.007157	0.000644	-0.055699	0.020002	0.002297	-0.012454	NaN	0.010690	-0.020185	-0.005779	-0.019459	-0.003803	-0.002305	-0.018214	-0.027656
MonthlyIncome	-0.159840	0.497855	0.007707	-0.017014	0.094961	NaN	-0.014829	-0.006259	-0.015794	-0.015271	0.950300	-0.007157	1.000000	0.034814	0.149515	-0.027269	-0.017120	0.025873	NaN	0.005408	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079
MonthlyRate	0.015170	0.028051	-0.032182	0.027473	-0.026084	NaN	0.012648	0.037600	-0.015297	-0.016322	0.039563	0.000644	0.034814	1.000000	0.017521	-0.006429	-0.009811	-0.004085	NaN	-0.034323	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746
NumCompaniesWorked	0.043494	0.299635	0.038153	-0.029251	0.126317	NaN	-0.001251	0.012594	0.022157	0.015012	0.142501	-0.055699	0.149515	0.017521	1.000000	-0.010238	-0.014095	0.052733	NaN	0.030075	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319
PercentSalaryHike	-0.013478	0.003634	0.022704	0.040235	-0.011111	NaN	-0.012944	-0.031701	-0.009062	-0.017205	-0.034730	0.020002	-0.027269	-0.006429	-0.010238	1.000000	0.773550	-0.040490	NaN	0.007528	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985
PerformanceRating	0.002889	0.001904	0.000473	0.027110	-0.024539	NaN	-0.020359	-0.029548	-0.002172	-0.029071	-0.021222	0.002297	-0.017120	-0.009811	-0.014095	0.773550	1.000000	-0.031351	NaN	0.003506	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827
RelationshipSatisfaction	-0.045872	0.053535	0.007846	0.006557	-0.009118	NaN	-0.069861	0.007665	0.001330	0.034297	0.021642	-0.012454	0.025873	-0.004085	0.052733	-0.040490	-0.031351	1.000000	NaN	-0.045952	0.024054	0.002497	0.019604	0.019367	-0.015123	0.033493	-0.000867
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	-0.137145	0.037510	0.042143	0.044872	0.018422	NaN	0.062227	0.003432	0.050263	0.021523	0.013984	0.010690	0.005408	-0.034323	0.030075	0.007528	0.003506	-0.045952	NaN	1.000000	0.010136	0.011274	0.004129	0.015058	0.050818	0.014352	0.024698
TotalWorkingYears	-0.171063	0.680381	0.014515	0.004628	0.148280	NaN	-0.014365	-0.002693	-0.002334	-0.005533	0.782208	-0.020185	0.772893	0.026442	0.237639	-0.020608	0.006744	0.024054	NaN	0.010136	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188
TrainingTimesLastYear	-0.059478	-0.019621	0.002453	-0.036942	-0.025100	NaN	0.023603	-0.019359	-0.008548	-0.015338	-0.018191	-0.005779	-0.021736	0.001467	-0.066054	-0.005221	-0.015579	0.002497	NaN	0.011274	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096
WorkLifeBalance	-0.063939	-0.021490	-0.037848	-0.026556	0.009819	NaN	0.010309	0.027627	-0.004607	-0.014617	0.037818	-0.019459	0.030683	0.007963	-0.008366	-0.003280	0.002572	0.019604	NaN	0.004129	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759
YearsAtCompany	-0.134392	0.311309	-0.034055	0.009508	0.069114	NaN	-0.011240	0.001458	-0.019582	-0.021355	0.534739	-0.003803	0.514285	-0.023655	-0.118421	-0.035991	0.003435	0.019367	NaN	0.015058	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212
YearsInCurrentRole	-0.160545	0.212901	0.009932	0.018845	0.060236	NaN	-0.008416	0.018007	-0.024106	0.008717	0.389447	-0.002305	0.363818	-0.012815	-0.090754	-0.001520	0.034986	-0.015123	NaN	0.050818	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365
YearsSinceLastPromotion	-0.033019	0.216513	-0.033229	0.010029	0.054254	NaN	-0.009019	0.016194	-0.026716	-0.024184	0.353885	-0.018214	0.344978	0.001567	-0.036814	-0.022154	0.017896	0.033493	NaN	0.014352	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224
YearsWithCurrManager	-0.156199	0.202089	-0.026363	0.014406	0.069065	NaN	-0.009197	-0.004999	-0.020123	0.025976	0.375281	-0.027656	0.344079	-0.036746	-0.110319	-0.011985	0.022827	-0.000867	NaN	0.024698	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000

6. Problem Statements

# Perform Test of Hypothesis :- Compare rates for same level male & female, check relationship between 
# categorical variable like Age & Gender / Gender & Education field, Age & Income etc

df_train6 = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')

df_train6.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

df_train6.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Attrition                 1470 non-null   int64 
 1   Age                       1470 non-null   int64 
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(27), object(8)
memory usage: 402.1+ KB

# 1] Comparing BusinessTravel vs Gender

BG = pd.crosstab(df_train5_cat1.BusinessTravel,df_train6.Gender)
BG

Gender	Female	Male
BusinessTravel
Non-Travel	49	101
Travel_Frequently	117	160
Travel_Rarely	422	621

from scipy.stats import chi2_contingency

chi_sqr, p_value, DOF, EXP = chi2_contingency(BG)

chi_sqr

4.031372310350092

p_value

0.13322895625828154

DOF

2

EXP

array([[ 60. ,  90. ],
       [110.8, 166.2],
       [417.2, 625.8]])

sns.countplot(x=df_train6.BusinessTravel,hue=df_train6.Gender)

<AxesSubplot: xlabel='BusinessTravel', ylabel='count'>

# 2] comparing Gender vs Monthlyrate

GM = pd.crosstab(df_train6.Gender,df_train6.MonthlyRate)
GM

MonthlyRate	2094	2097	2104	2112	2122	2125	2137	2227	2243	2253	2261	2288	2302	2323	2326	2338	2354	2373	2396	2437	2447	2493	2539	2560	2561	2613	2671	2689	2690	2706	2721	2725	2739	2755	2819	2823	2845	2851	2890	2900	2912	2939	2967	2975	2993	2997	3010	3020	3031	3032	3064	3072	3088	3119	3129	3140	3142	3156	3157	3164	3173	3193	3208	3297	3300	3334	3335	3339	3356	3372	3376	3395	3415	3423	3425	3427	3445	3449	3458	3465	3487	3498	3525	3536	3549	3567	3622	3666	3687	3692	3698	3708	3735	3787	3809	3810	3811	3835	3840	3854	...	24852	24907	24920	24941	24978	25043	25063	25098	25103	25150	25166	25174	25178	25198	25233	25258	25265	25275	25291	25308	25326	25348	25353	25388	25412	25422	25440	25470	25479	25518	25527	25549	25592	25594	25605	25657	25681	25713	25725	25751	25755	25761	25796	25800	25811	25812	25846	25927	25949	25952	25995	26009	26062	26075	26076	26085	26092	26124	26176	26186	26204	26227	26236	26250	26278	26283	26285	26308	26312	26314	26342	26362	26376	26427	26458	26493	26496	26507	26537	26542	26551	26582	26589	26619	26703	26707	26767	26820	26841	26849	26862	26894	26897	26914	26933	26956	26959	26968	26997	26999
Gender
Female	1	0	0	1	0	0	1	1	0	0	1	0	1	1	0	1	1	0	0	1	0	1	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	0	1	0	0	1	0	0	0	0	1	1	0	1	1	0	0	0	0	0	1	1	1	0	0	1	0	0	0	0	1	1	1	0	0	0	0	0	0	0	1	...	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	1	0	1	1	1	1	0	0	0	0	0	1	0	1	1	0	1	1	1	0	0	0	1	1	0	0	1	1	0	0	0	0	0	0	0	1	0	0	1	0	1	0	0	1	0	0	0	0	0	1	1	0	0	1	1	0	0	1	0	0	0	0	0	1	1	1	1	0	1	1	1	1	0	1	1	0	0	1	1	0
Male	0	1	1	0	1	2	0	0	1	1	0	1	0	0	1	0	0	1	1	0	1	0	1	1	0	1	1	1	1	1	1	1	0	2	1	1	0	1	0	1	0	1	1	1	1	1	1	1	1	0	1	0	1	1	0	0	1	1	1	1	1	0	1	1	0	1	1	2	1	0	0	1	0	0	1	1	1	1	1	0	0	0	1	1	0	1	1	1	1	0	0	0	1	1	1	1	1	1	1	0	...	1	1	0	0	1	1	1	1	1	1	1	1	1	0	1	1	0	1	1	0	1	1	0	0	0	0	1	1	1	1	1	0	1	0	0	1	0	0	0	1	1	1	0	0	1	1	0	0	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1	0	1	1	1	1	1	0	0	1	1	0	0	1	1	0	1	1	1	1	1	0	0	0	0	1	0	0	0	0	1	0	0	1	1	0	0	1

2 rows × 1427 columns

chi_sqr, p_value, DOF, EXP = chi2_contingency(GM)

chi_sqr

1421.388888888889

p_value

0.5294748183389015

DOF

1426

# 3] comparing Gender vs Department

GD = pd.crosstab(df_train6.Gender,df_train6.Department)
GD

Department	Human Resources	Research & Development	Sales
Gender
Female	20	379	189
Male	43	582	257

plt.figure(figsize=(10,9))
sns.countplot(x=df_train6.Gender,hue=df_train6.Department)

<AxesSubplot: xlabel='Gender', ylabel='count'>

# 4] Comparing Gender vs Age

GA = pd.crosstab(df_train6.Gender,df_train6.Age)
GA

Age	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60
Gender
Female	4	4	6	6	5	1	11	6	14	22	12	29	26	28	23	24	33	27	29	18	27	16	24	15	16	14	10	17	15	14	7	12	9	7	7	10	15	4	7	0	6	6	2
Male	4	5	5	7	11	13	15	20	25	26	36	39	34	41	38	34	44	51	40	32	31	26	33	25	30	18	23	24	18	10	12	12	21	12	11	9	3	18	7	4	8	4	3

chi_sqr, p_value, DOF, EXP = chi2_contingency(GA)

chi_sqr

53.537114950915246

p_value

0.10927801670328505

# 5] comparing Overtime and Age

OA = pd.crosstab(df_train6.OverTime,df_train6.Age)
OA

Age	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48	49	50	51	52	53	54	55	56	57	58	59	60
OverTime
No	6	5	7	10	9	11	18	18	27	36	36	49	48	49	50	40	60	52	53	34	40	33	42	27	31	24	18	31	25	14	17	18	17	15	13	14	15	12	11	3	5	7	4
Yes	2	4	4	3	7	3	8	8	12	12	12	19	12	20	11	18	17	26	16	16	18	9	15	13	15	8	15	10	8	10	2	6	13	4	5	5	3	10	3	1	9	3	1

chi_sqr, p_value, DOF, EXP = chi2_contingency(OA)

chi_sqr

44.366828521428644

p_value

0.37220025607537444

# 6] Compairing Maritalstatus and Gender

MG=pd.crosstab(df_train6.MaritalStatus,df_train6.Gender)
MG

Gender	Female	Male
MaritalStatus
Divorced	117	210
Married	272	401
Single	199	271

chi_sqr, p_value, DOF, EXP = chi2_contingency(MG)

chi_sqr

3.5478394206821307

p_value

0.1696666396487212

plt.figure(figsize=(10,9))
sns.countplot(x=df_train6.MaritalStatus, hue = df_train6.Gender)

<AxesSubplot: xlabel='MaritalStatus', ylabel='count'>

# Compare age and Monthly income

AM = pd.crosstab(df_train6.Age,df_train6.MonthlyIncome)
AM.head()

MonthlyIncome	1009	1051	1052	1081	1091	1102	1118	1129	1200	1223	1232	1261	1274	1281	1359	1393	1416	1420	1483	1514	1555	1563	1569	1601	1611	1675	1702	1706	1790	1859	1878	1904	1951	2001	2007	2008	2011	2013	2014	2018	2022	2024	2028	2029	2033	2042	2044	2045	2058	2061	2062	2064	2066	2070	2073	2074	2075	2080	2083	2086	2088	2089	2090	2093	2096	2097	2099	2105	2107	2109	2115	2119	2121	2127	2132	2133	2141	2143	2144	2145	2148	2153	2154	2157	2166	2168	2174	2176	2177	2180	2187	2194	2201	2206	2207	2210	2213	2216	2218	2220	...	16856	16872	16880	16885	16959	17007	17046	17048	17068	17099	17123	17159	17169	17174	17181	17328	17399	17426	17444	17465	17567	17584	17603	17639	17650	17665	17779	17856	17861	17875	17924	18041	18061	18172	18200	18213	18265	18300	18303	18430	18606	18665	18711	18722	18740	18789	18824	18844	18880	18947	19033	19038	19045	19049	19068	19081	19094	19141	19144	19161	19187	19189	19190	19197	19202	19232	19237	19246	19272	19328	19331	19392	19406	19419	19431	19436	19502	19513	19517	19537	19545	19566	19586	19613	19626	19627	19636	19658	19665	19701	19717	19740	19833	19845	19847	19859	19926	19943	19973	19999
Age
18	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	0	1	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
19	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
20	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
21	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

5 rows × 1349 columns

chi_sqr, p_value, DOF, EXP = chi2_contingency(AM)

chi_sqr

57003.01607044047

p_value

0.12517635416744993

# Comparing Educationfield and Gender

EG = pd.crosstab(df_train6.EducationField,df_train6.Gender)
EG.head()

Gender	Female	Male
EducationField
Human Resources	8	19
Life Sciences	240	366
Marketing	69	90
Medical	190	274
Other	29	53

chi_sqr,p_value,DOF,EXP = chi2_contingency(EG)

chi_sqr

2.9414238793151797

p_value

0.7090162522843911

plt.figure(figsize=(10,9))
sns.countplot(x=df_train6.EducationField,hue=df_train6.Gender)

<AxesSubplot: xlabel='EducationField', ylabel='count'>

# Comparing Hourly rate and Age

ht = pd.crosstab(df_train6.HourlyRate,df_train6.Gender)
ht

Gender	Female	Male
HourlyRate
30	11	8
31	8	7
32	8	16
33	12	7
34	5	7
...	...	...
96	12	15
97	7	14
98	13	15
99	7	13
100	7	12

71 rows × 2 columns

chi_sqr,p_value,DOF,EXP = chi2_contingency(ht)

chi_sqr

66.92505276398174

p_value

0.5820716429499165

7.1 perform Regression treating Monthly Rate as Y, and choose predictions error and the best model

# Use 1] SLR, 2] MLR, 3] Polynomial Regression, 4] Interaction term, 5] Ridge Regression, 6] Lasso etc

7.1] SLR

DF_S = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')
DF_S.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

# Splitting the datasets into training and testing datasets

x = DF_S[['DailyRate']]
y = DF_S[['MonthlyRate']]

x

	DailyRate
0	1102
1	279
2	1373
3	1392
4	591
...	...
1465	884
1466	613
1467	155
1468	1023
1469	628

1470 rows × 1 columns

y

	MonthlyRate
0	19479
1	24907
2	2396
3	23159
4	16632
...	...
1465	12290
1466	21457
1467	5174
1468	13243
1469	10228

1470 rows × 1 columns

xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.20,random_state=31)

xtrain

	DailyRate
815	984
1187	1189
1321	207
728	1441
387	759
...	...
826	433
610	269
894	685
16	334
722	1391

1176 rows × 1 columns

xtest

	DailyRate
1343	592
334	549
1136	329
1080	228
396	1473
...	...
91	632
520	817
1403	119
479	1287
1135	563

294 rows × 1 columns

# Model Building

lin_model = LinearRegression()
lin_model.fit(xtrain,ytrain)

LinearRegression()

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# Training data evaluation
ypredtrain = lin_model.predict(xtrain)

MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)

MSE = mean_squared_error(ytrain,ypredtrain)
print('Mean squared error:',MSE)

RMSE = np.sqrt(MSE)
print('Root mean squared error:',RMSE)

RSquared = r2_score(ytrain,ypredtrain)
print('R-Squared:',RSquared)

AdjRsquared = 1-((1-RSquared)*(len(xtrain)-1)/(len(xtrain)-len(x.columns)-1))
print('AdjRsquared:',AdjRsquared)

Mean absolute error: 6270.772185623379
Mean squared error: 51623223.78228901
Root mean squared error: 7184.930325499964
R-Squared: 0.0010404518598107204
AdjRsquared: 0.0001895493486180344

# Testing data evaluation
ypredtest = lin_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)

MSE = mean_squared_error(ytest,ypredtest)
print('Mean squared error:',MSE)

RMSE = np.sqrt(MSE)
print('Root mean squared error:',RMSE)

RSquared = r2_score(ytest,ypredtest)
print('R-Squared:',RSquared)

AdjRsquared = 1-((1-RSquared)*(len(xtest)-1)/(len(xtest)-len(x.columns)-1))
print('AdjRsquared:',AdjRsquared)

Mean absolute error: 5849.367272215098
Mean squared error: 46390905.9967264
Root mean squared error: 6811.08699083534
R-Squared: 0.0007109055817979826
AdjRsquared: -0.0027113173442918637

7.2 MLR

# Read the data

df_7 = pd.read_csv(r'G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')
df_7.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

y = df_7[['MonthlyRate']]

df_7tr = df_7.drop('MonthlyRate',axis=1)

df_7tr

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	Travel_Frequently	884	Research & Development	23	2	Medical	1	2061	3	Male	41	4	2	Laboratory Technician	4	Married	2571	4	Y	No	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	Travel_Rarely	613	Research & Development	6	1	Medical	1	2062	4	Male	42	2	3	Healthcare Representative	1	Married	9991	4	Y	No	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	Travel_Rarely	155	Research & Development	4	3	Life Sciences	1	2064	2	Male	87	4	2	Manufacturing Director	2	Married	6142	1	Y	Yes	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	Travel_Frequently	1023	Sales	2	3	Medical	1	2065	4	Male	63	2	2	Sales Executive	2	Married	5390	2	Y	No	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	Travel_Rarely	628	Research & Development	8	3	Medical	1	2068	2	Male	82	4	2	Laboratory Technician	3	Married	4404	2	Y	No	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 34 columns

# Seprate Categorical and continious columns

cat = []
con = []
for i in df_7tr.columns:
    if (df_7tr[i].dtypes == 'object'):
        cat.append(i)
    else:
        con.append(i)

df_train7_con = con
df_train7_con = df_7tr[con]
df_train7_con

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	2	4	5993	8	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	2	2	5130	1	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	1	3	2090	6	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	1	3	2909	1	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	1	2	3468	9	12	3	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	2	4	2571	4	17	3	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	3	1	9991	4	15	3	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	2	2	6142	1	20	4	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	2	2	5390	2	14	3	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	2	3	4404	2	12	3	1	80	0	6	3	4	4	3	1	2

1470 rows × 26 columns

df_train7_cat = cat
df_train7_cat = df_7tr[cat]
df_train7_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

# Visulaisation of categorical columns 

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train7_cat.columns):
    if df_train7_cat[i].dtypes=='object':
        plt.subplot(3,3,x1+1)
        sns.countplot(x=df_train7_cat[i])

# Visualisation continiuos columns

plt.figure(figsize=(17,19))
for x1,i in enumerate(df_train7_con.columns):
    if df_train7_con[i].dtypes=='int64' or df_train7_con[i].dtypes=='float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train7_con[i])

for i in df_train7_con.columns:
    q1 = df_train7_con[i].quantile(0.25)
    q3 = df_train7_con[i].quantile(0.75)
    IQR = q3-q1
    uppertail = q3+1.5*IQR
    lowertail = q1-1.5*IQR
    df_train7_con.loc[(df_train7_con[i]>uppertail) | (df_train7_con[i]<lowertail)]
    mean_7 = df_train7_con[i].mean()
    df_train7_con.loc[(df_train7_con[i]>uppertail) | (df_train7_con[i]<lowertail),i],mean_7

plt.figure(figsize=(17,19))
for x1,i in enumerate(df_train7_con.columns):
    if df_train7_con[i].dtypes=='int64' or df_train7_con[i].dtypes=='float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train7_con[i])

# One hot encoding

df_train7_dum = pd.get_dummies(df_train7_cat)

df_train7_dum.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0

# Standardization

std_scaler = StandardScaler()
std_scaler1 = std_scaler.fit_transform(df_train7_con)

x_lin = pd.DataFrame(std_scaler1,columns=df_train7_con.columns)

x_lin.head()

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227

df_merge_7 = pd.concat([df_train7_dum,x_lin],axis=1)
df_merge_7

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	EducationField_Other	EducationField_Technical Degree	Gender_Female	Gender_Male	JobRole_Healthcare Representative	JobRole_Human Resources	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	0	0	1	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	1	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	0	0	1	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	1	1	0	1	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	0	1	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	0	0	1	0	1	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	0	0	1	0	1	0	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	-0.438422	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	0	1	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	1	1	0	-0.438422	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	0	0	1	0	1	0	0	0	0	1	0	0	0	1	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	-0.438422	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 55 columns

# VIF
from statsmodels.stats.outliers_influence import variance_inflation_factor
vif7l = pd.DataFrame()
vif7l['Features'] = df_merge_7.columns
vif7l['VIF'] = [variance_inflation_factor(df_merge_7.values,i) for i in range(df_merge_7.shape[1])]
vif7l

	Features	VIF
0	BusinessTravel_Non-Travel	inf
1	BusinessTravel_Travel_Frequently	inf
2	BusinessTravel_Travel_Rarely	inf
3	Department_Human Resources	inf
4	Department_Research & Development	inf
5	Department_Sales	inf
6	EducationField_Human Resources	inf
7	EducationField_Life Sciences	inf
8	EducationField_Marketing	inf
9	EducationField_Medical	inf
10	EducationField_Other	inf
11	EducationField_Technical Degree	inf
12	Gender_Female	inf
13	Gender_Male	inf
14	JobRole_Healthcare Representative	inf
15	JobRole_Human Resources	inf
16	JobRole_Laboratory Technician	inf
17	JobRole_Manager	inf
18	JobRole_Manufacturing Director	inf
19	JobRole_Research Director	inf
20	JobRole_Research Scientist	inf
21	JobRole_Sales Executive	inf
22	JobRole_Sales Representative	inf
23	MaritalStatus_Divorced	inf
24	MaritalStatus_Married	inf
25	MaritalStatus_Single	inf
26	Over18_Y	0.000000
27	OverTime_No	inf
28	OverTime_Yes	inf
29	Attrition	1.347205
30	Age	2.093292
31	DailyRate	1.038089
32	DistanceFromHome	1.031397
33	Education	1.084381
34	EmployeeCount	NaN
35	EmployeeNumber	1.035822
36	EnvironmentSatisfaction	1.048560
37	HourlyRate	1.027678
38	JobInvolvement	1.049355
39	JobLevel	14.152924
40	JobSatisfaction	1.043983
41	MonthlyIncome	18.134627
42	NumCompaniesWorked	1.300624
43	PercentSalaryHike	2.565100
44	PerformanceRating	2.550958
45	RelationshipSatisfaction	1.038662
46	StandardHours	NaN
47	StockOptionLevel	1.923492
48	TotalWorkingYears	5.019051
49	TrainingTimesLastYear	1.040440
50	WorkLifeBalance	1.034209
51	YearsAtCompany	4.760278
52	YearsInCurrentRole	2.805982
53	YearsSinceLastPromotion	1.729502
54	YearsWithCurrManager	2.856316

featuresdrop = vif7l.loc[vif7l['VIF']>10]
droplist = featurestodrop['Features']
droplist = list(droplist)
len(droplist)
print(droplist)

['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely', 'Department_Human Resources', 'Department_Research & Development', 'Department_Sales', 'EducationField_Human Resources', 'EducationField_Life Sciences', 'EducationField_Marketing', 'EducationField_Medical', 'EducationField_Other', 'EducationField_Technical Degree', 'Gender_Female', 'Gender_Male', 'JobRole_Healthcare Representative', 'JobRole_Human Resources', 'JobRole_Laboratory Technician', 'JobRole_Manager', 'JobRole_Manufacturing Director', 'JobRole_Research Director', 'JobRole_Research Scientist', 'JobRole_Sales Executive', 'JobRole_Sales Representative', 'MaritalStatus_Divorced', 'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_No', 'OverTime_Yes']

drop_list7 = ['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently', 'BusinessTravel_Travel_Rarely', 
              'Department_Human Resources', 'Department_Research & Development', 'Department_Sales',
              'EducationField_Human Resources','EducationField_Life Sciences', 'EducationField_Marketing', 
              'EducationField_Medical', 'EducationField_Other','EducationField_Technical Degree', 'Gender_Female', 
              'Gender_Male', 'JobRole_Healthcare Representative','JobRole_Human Resources', 
              'JobRole_Laboratory Technician', 'JobRole_Manager', 'JobRole_Manufacturing Director', 
              'JobRole_Research Director', 'JobRole_Research Scientist', 'JobRole_Sales Executive', 
              'JobRole_Sales Representative','MaritalStatus_Divorced', 'MaritalStatus_Married','MaritalStatus_Single', 
              'OverTime_No', 'OverTime_Yes']

df_lin_7 = df_merge_7.drop(drop_list7,axis=1)

df_lin_7

	Over18_Y	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	2.280906	0.446350	0.742527	-1.010909	-0.891688	0.0	-1.701283	-0.660531	1.383138	0.379672	-0.057788	1.153254	-0.108350	2.125136	-1.150554	-0.426230	-1.584178	0.0	-0.932014	-0.421642	-2.171982	-2.493820	-0.164613	-0.063296	-0.679146	0.245834
1	1	-0.438422	1.322365	-1.297775	-0.147150	-1.868426	0.0	-1.699621	0.254625	-0.240677	-1.026167	-0.057788	-0.660853	-0.291719	-0.678049	2.129306	2.346151	1.191438	0.0	0.241988	-0.164511	0.155707	0.338096	0.488508	0.764998	-0.368715	0.806541
2	1	2.280906	0.008343	1.414363	-0.887515	-0.891688	0.0	-1.696298	1.169781	1.284725	-1.026167	-0.961486	0.246200	-0.937654	1.324226	-0.057267	-0.426230	-0.658973	0.0	-0.932014	-0.550208	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
3	1	-0.438422	-0.429664	1.461466	-0.764121	1.061787	0.0	-1.694636	1.169781	-0.486709	0.379672	-0.961486	0.246200	-0.763634	-0.678049	-1.150554	-0.426230	0.266233	0.0	-0.932014	-0.421642	0.155707	0.338096	0.161947	0.764998	0.252146	-1.155935
4	1	-0.438422	-1.086676	-0.524295	-0.887515	-1.868426	0.0	-1.691313	-1.575686	-1.274014	0.379672	-0.961486	-0.660853	-0.644858	2.525591	-0.877232	-0.426230	1.191438	0.0	0.241988	-0.678774	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	1	-0.438422	-0.101159	0.202082	1.703764	-0.891688	0.0	1.721670	0.254625	-1.224807	1.785511	-0.057788	1.153254	-0.835451	0.523316	0.489376	-0.426230	0.266233	0.0	0.241988	0.735447	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.314873
1466	1	-0.438422	0.227347	-0.469754	-0.393938	-1.868426	0.0	1.723332	1.169781	-1.175601	-1.026167	0.845911	-1.567907	0.741140	0.523316	-0.057267	-0.426230	-1.584178	0.0	0.241988	-0.293077	1.707500	0.338096	-0.001333	0.764998	-0.368715	0.806541
1467	1	-0.438422	-1.086676	-1.605183	-0.640727	0.085049	0.0	1.726655	-0.660531	1.038693	1.785511	-0.057788	-0.660853	-0.076690	-0.678049	1.309341	2.346151	-0.658973	0.0	0.241988	-0.678774	-2.171982	0.338096	-0.164613	-0.615492	-0.679146	-0.314873
1468	1	-0.438422	1.322365	0.546677	-0.887515	0.085049	0.0	1.728317	1.169781	-0.142264	-1.026167	-0.057788	-0.660853	-0.236474	-0.277594	-0.330589	-0.426230	1.191438	0.0	-0.932014	0.735447	0.155707	-1.077862	0.325228	0.488900	-0.679146	1.086895
1469	1	-0.438422	-0.320163	-0.432568	-0.147150	0.085049	0.0	1.733302	-0.660531	0.792660	1.785511	-0.057788	0.246200	-0.445978	-0.277594	-0.877232	-0.426230	-1.584178	0.0	-0.932014	-0.678774	0.155707	1.754054	-0.491174	-0.339394	-0.368715	-0.595227

1470 rows × 27 columns

# Splitting the datasets
xtrain,xtest,ytrain,ytest = train_test_split(df_lin_7,y,test_size=0.25,random_state=31)

# Model Building

lin_model = LinearRegression()
lin_model.fit(xtrain,ytrain)

LinearRegression()

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# Training data evaluation
ypredtrain = lin_model.predict(xtrain)

MAE = mean_absolute_error(ytrain,ypredtrain)
print('Mean absolute error:',MAE)

MSE = mean_squared_error(ytrain,ypredtrain)
print('Mean squared error:',MSE)

RMSE = np.sqrt(MSE)
print('Root mean squared error:',RMSE)

RSquared = r2_score(ytrain,ypredtrain)
print('R-Squared:',RSquared)

AdjRsquared = 1-((1-RSquared)*(len(xtrain)-1)/(len(xtrain)-len(x_lin.columns)-1))
print('AdjRsquared:',AdjRsquared)

Mean absolute error: 6207.680039002682
Mean squared error: 50822766.188194014
Root mean squared error: 7129.008780201776
R-Squared: 0.012529315004031982
AdjRsquared: -0.011353696912149713

# testing data evaluation
ypredtest = lin_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('Mean absolute error:',MAE)

MSE = mean_squared_error(ytest,ypredtest)
print('Mean squared error:',MSE)

RMSE = np.sqrt(MSE)
print('Root mean squared error:',RMSE)

RSquared = r2_score(ytest,ypredtest)
print('R-Squared:',RSquared)

AdjRsquared = 1-((1-RSquared)*(len(xtest)-1)/(len(xtest)-len(x_lin.columns)-1))
print('AdjRsquared:',AdjRsquared)

Mean absolute error: 6021.015273843959
Mean squared error: 48787827.28982247
Root mean squared error: 6984.828365094053
R-Squared: -0.01442841066218259
AdjRsquared: -0.09177485839595612

7.5 Lasso

lasso = Lasso()
lasso.fit(xtrain,ytrain)

Lasso()

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# Testing Data Evaluation
ypredtest = lasso.predict(xtest)

Mean_squared_error = mean_squared_error(ytest,ypredtest)
print('Mean Squared Error:',Mean_squared_error)

r2Score = r2_score(ytest,ypredtest)
print('R2-Score:',r2Score)

Mean Squared Error: 48774725.24318157
R2-Score: -0.014155984340112893

# training data evaluation
ypredtrain = lasso.predict(xtrain)

Mean_squared_error = mean_squared_error(ytrain,ypredtrain)
print('Mean Squared Error:',Mean_squared_error)

r2Score = r2_score(ytrain,ypredtrain)
print('R2-Score:',r2Score)

Mean Squared Error: 50822852.237603806
R2-Score: 0.01252764309051757

7.6 Ridge

ridge = Ridge()
ridge.fit(xtrain,ytrain)

Ridge()

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# Testing Data Evaluation
ypredtest = ridge.predict(xtest)

Mean_squared_error = mean_squared_error(ytest,ypredtest)
print('Mean Squared Error:',Mean_squared_error)

r2Score = r2_score(ytest,ypredtest)
print('R2-Score:',r2Score)

Mean Squared Error: 48784711.04356809
R2-Score: -0.01436361563213806

#Training Data Evaluation
ypredtrain = ridge.predict(xtrain)

Mean_squared_error = mean_squared_error(ytrain,ypredtrain)
print('Mean Squared Error:',Mean_squared_error)

r2Score = r2_score(ytrain,ypredtrain)
print('R2-Score:',r2Score)

Mean Squared Error: 50822774.823542684
R2-Score: 0.012529147221868486

7.2 Use KNN

xtrain,xtest,ytrain,ytest = train_test_split(df_lin_7,y,test_size=0.25,random_state=31)

xtrain

	Over18_Y	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1074	1	-0.438422	-0.429664	-0.710227	-0.147150	2.038524	0.0	0.814421	1.169781	0.152975	0.379672	-0.057788	0.246200	-0.024420	-0.277594	0.489376	-0.42623	-1.584178	0.0	-0.932014	0.349751	2.483396	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
1458	1	-0.438422	-0.210661	-1.277942	-1.010909	1.061787	0.0	1.706715	0.254625	-0.191470	-2.432006	-0.961486	1.153254	-0.749185	-0.678049	-0.877232	-0.42623	1.191438	0.0	0.241988	-0.935905	1.707500	0.338096	-0.491174	-0.339394	-0.368715	-0.875581
414	1	2.280906	-1.415181	1.600296	-1.010909	-1.868426	0.0	-0.782403	-1.575686	-0.191470	0.379672	-0.961486	-0.660853	-0.701377	-0.678049	0.216054	-0.42623	-0.658973	0.0	-0.932014	-0.678774	0.931603	0.338096	-0.327893	-0.339394	-0.368715	-0.034520
1371	1	-0.438422	2.088878	1.587900	0.223033	2.038524	0.0	1.512305	1.169781	1.137106	-1.026167	-0.057788	-1.567907	-0.238599	0.523316	0.216054	-0.42623	0.266233	0.0	0.241988	-0.678774	0.155707	0.338096	-1.144294	-1.167687	-0.679146	-1.155935
1140	1	-0.438422	0.774856	1.265617	-0.270544	0.085049	0.0	0.968953	-0.660531	-1.716872	0.379672	2.653309	1.153254	2.665772	-1.078504	-0.330589	-0.42623	1.191438	0.0	0.241988	1.506840	0.931603	-1.077862	2.447870	0.764998	-0.368715	1.647603
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
826	1	-0.438422	0.117845	-0.915993	-1.010909	0.085049	0.0	0.211251	0.254625	-1.421633	1.785511	-0.961486	0.246200	-0.777445	-0.678049	-0.603911	-0.42623	1.191438	0.0	0.241988	-0.550208	-0.620189	1.754054	-0.001333	0.488900	0.873006	-1.155935
610	1	-0.438422	-1.086676	-1.322566	-0.517332	-1.868426	0.0	-0.300531	0.254625	-1.175601	-1.026167	0.845911	1.153254	1.339692	-0.678049	0.216054	-0.42623	-0.658973	0.0	0.241988	-0.293077	0.155707	0.338096	0.325228	1.041095	-0.679146	1.086895
894	1	-0.438422	1.869874	-0.291259	-0.764121	0.085049	0.0	0.374090	1.169781	0.940280	0.379672	1.749610	1.153254	2.395924	0.122861	-0.330589	-0.42623	-1.584178	0.0	-0.932014	3.178192	-0.620189	0.338096	0.488508	1.317193	-0.679146	1.367249
16	1	-0.438422	-0.539166	-1.161424	-0.517332	-0.891688	0.0	-1.668050	-1.575686	0.694247	1.785511	-0.961486	-0.660853	-0.680979	-1.078504	-0.877232	-0.42623	1.191438	0.0	1.415991	-0.550208	1.707500	-1.077862	-0.164613	-0.615492	-0.679146	0.245834
722	1	-0.438422	0.117845	1.458987	0.099639	-1.868426	0.0	-0.031347	0.254625	0.005356	0.379672	-0.961486	0.246200	-0.811441	-1.078504	0.489376	-0.42623	-0.658973	0.0	0.241988	-1.064470	-2.171982	-1.077862	-0.817734	-0.891589	-0.679146	-0.595227

1102 rows × 27 columns

xtest

	Over18_Y	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1343	1	-0.438422	-0.867672	-0.521816	-0.270544	0.085049	0.0	1.425900	1.169781	-0.339090	0.379672	-0.961486	-1.567907	-0.943603	0.122861	-0.330589	-0.426230	-0.658973	0.0	-0.932014	-0.035946	-0.620189	0.338096	-0.654454	-0.615492	-0.368715	-0.595227
334	1	-0.438422	0.884358	-0.628417	-0.147150	1.061787	0.0	-0.951889	1.169781	0.448214	0.379672	-0.057788	1.153254	-0.596200	2.525591	-0.330589	-0.426230	-1.584178	0.0	1.415991	0.092620	0.155707	0.338096	0.488508	1.317193	2.114728	1.086895
1136	1	2.280906	-0.977174	-1.173819	1.827158	0.085049	0.0	0.962306	0.254625	-0.732742	0.379672	-0.961486	-0.660853	-0.870085	-0.678049	0.489376	-0.426230	0.266233	0.0	2.589994	-1.321601	0.155707	0.338096	-0.981014	-0.891589	-0.679146	-1.155935
1080	1	-0.438422	0.993860	-1.424209	-0.764121	0.085049	0.0	0.834361	0.254625	-0.732742	0.379672	1.749610	-0.660853	2.146686	2.125136	-0.877232	-0.426230	1.191438	0.0	0.241988	1.506840	-0.620189	1.754054	0.978348	2.145487	0.873006	-0.875581
396	1	-0.438422	0.665354	1.662273	-0.147150	1.061787	0.0	-0.828928	0.254625	0.399008	0.379672	-0.057788	0.246200	-0.420906	0.523316	-0.330589	-0.426230	1.191438	0.0	-0.932014	-0.421642	0.155707	0.338096	-0.327893	-0.615492	-0.679146	-0.595227
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
808	1	-0.438422	-0.867672	0.754922	2.320735	1.061787	0.0	0.158078	0.254625	1.333932	0.379672	-0.961486	1.153254	-0.847563	0.523316	1.855984	2.346151	-1.584178	0.0	0.241988	-0.035946	-1.396086	0.338096	-0.001333	0.212802	-0.368715	0.806541
1445	1	-0.438422	0.446350	-0.546607	2.320735	1.061787	0.0	1.676806	-1.575686	-0.289883	-1.026167	1.749610	-0.660853	1.501601	-1.078504	2.129306	2.346151	0.266233	0.0	0.241988	1.249709	0.155707	0.338096	2.121310	0.764998	-0.679146	1.647603
1298	1	2.280906	0.993860	-1.342398	1.456975	-0.891688	0.0	1.322879	1.169781	0.005356	0.379672	-0.057788	-0.660853	0.514850	0.523316	1.855984	2.346151	1.191438	0.0	0.241988	0.221185	-0.620189	1.754054	0.325228	0.764998	0.252146	0.806541
29	1	-0.438422	0.993860	-0.241677	-0.887515	1.061787	0.0	-1.639803	-0.660531	0.841867	0.379672	2.653309	-1.567907	2.644099	0.122861	-0.877232	-0.426230	1.191438	0.0	-0.932014	1.378275	-0.620189	-1.077862	-0.817734	-0.615492	-0.058285	-0.875581
867	1	-0.438422	1.431867	1.533360	-0.887515	0.085049	0.0	0.315933	1.169781	-1.766079	0.379672	1.749610	-1.567907	2.412285	-0.277594	1.855984	2.346151	0.266233	0.0	0.241988	2.663930	0.155707	0.338096	-0.817734	-0.615492	-0.058285	-0.595227

368 rows × 27 columns

ytrain

	MonthlyRate
1074	22049
1458	8952
414	21972
1371	20328
1140	3549
...	...
826	6004
610	8842
894	23474
16	15053
722	12127

1102 rows × 1 columns

knn_model = KNeighborsRegressor()
knn_model.fit(xtrain,ytrain)

KNeighborsRegressor()

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# Training data evaluation

ypredtrain = knn_model.predict(xtrain)

MAE = mean_absolute_error(ytrain,ypredtrain)
print('MAE:', MAE)

MSE = mean_squared_error(ytrain,ypredtrain)
print('MSE:',MSE)

RMSE = np.sqrt(MSE)
print('RMSE:',RMSE)

RS = r2_score(ytrain,ypredtrain)
print('RS:',RS)

ADJS = 1-((1*RS)*(len(xtrain)-1)/(len(xtrain)-len(x_lin.columns)-1))
print('ADJS:',ADJS)

MAE: 5516.821052631579
MSE: 42177305.469255894
RMSE: 6494.405705625103
RS: 0.18050795250327756
ADJS: 0.8151262737617595

# testing data evaluation

ypredtest = knn_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('MAE:', MAE)

MSE = mean_squared_error(ytest,ypredtest)
print('MSE:',MSE)

RMSE = np.sqrt(MSE)
print('RMSE:',RMSE)

RS = r2_score(ytest,ypredtest)
print('RS:',RS)

ADJS = 1-((1*RS)*(len(xtest)-1)/(len(xtest)-len(x_lin.columns)-1))
print('ADJS:',ADJS)

MAE: 6257.771739130435
MSE: 56677783.95021739
RMSE: 7528.464913262025
RS: -0.17848154932014415
ADJS: 1.1920901131979265

min_max_scaler = MinMaxScaler()
array = min_max_scaler.fit_transform(x_lin)
df_norm = pd.DataFrame(array,columns=x_lin.columns)
df_norm

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	JobSatisfaction	MonthlyIncome	NumCompaniesWorked	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1.0	0.547619	0.715820	0.000000	0.25	0.0	0.000000	0.333333	0.914286	0.666667	0.25	1.000000	0.262454	0.888889	0.000000	0.0	0.000000	0.0	0.000000	0.200	0.000000	0.000000	0.150	0.222222	0.000000	0.294118
1	0.0	0.738095	0.126700	0.250000	0.00	0.0	0.000484	0.666667	0.442857	0.333333	0.25	0.333333	0.217009	0.111111	0.857143	1.0	1.000000	0.0	0.333333	0.250	0.500000	0.666667	0.250	0.388889	0.066667	0.411765
2	1.0	0.452381	0.909807	0.035714	0.25	0.0	0.001451	1.000000	0.885714	0.333333	0.00	0.666667	0.056925	0.666667	0.285714	0.0	0.333333	0.0	0.000000	0.175	0.500000	0.666667	0.000	0.000000	0.000000	0.000000
3	0.0	0.357143	0.923407	0.071429	0.75	0.0	0.001935	1.000000	0.371429	0.666667	0.00	0.666667	0.100053	0.111111	0.000000	0.0	0.666667	0.0	0.000000	0.200	0.500000	0.666667	0.200	0.388889	0.200000	0.000000
4	0.0	0.214286	0.350036	0.035714	0.00	0.0	0.002903	0.000000	0.142857	0.666667	0.00	0.333333	0.129489	1.000000	0.071429	0.0	1.000000	0.0	0.333333	0.150	0.500000	0.666667	0.050	0.111111	0.133333	0.117647
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0.0	0.428571	0.559771	0.785714	0.25	0.0	0.996613	0.666667	0.157143	1.000000	0.25	1.000000	0.082254	0.444444	0.428571	0.0	0.666667	0.0	0.333333	0.425	0.500000	0.666667	0.125	0.111111	0.000000	0.176471
1466	0.0	0.500000	0.365784	0.178571	0.00	0.0	0.997097	1.000000	0.171429	0.333333	0.50	0.000000	0.472986	0.444444	0.285714	0.0	0.000000	0.0	0.333333	0.225	0.833333	0.666667	0.175	0.388889	0.066667	0.411765
1467	0.0	0.214286	0.037938	0.107143	0.50	0.0	0.998065	0.333333	0.814286	1.000000	0.25	0.333333	0.270300	0.111111	0.642857	1.0	0.333333	0.0	0.333333	0.150	0.000000	0.666667	0.150	0.111111	0.000000	0.176471
1468	0.0	0.738095	0.659270	0.035714	0.50	0.0	0.998549	1.000000	0.471429	0.333333	0.25	0.333333	0.230700	0.222222	0.214286	0.0	1.000000	0.0	0.000000	0.425	0.500000	0.333333	0.225	0.333333	0.000000	0.470588
1469	0.0	0.380952	0.376521	0.250000	0.50	0.0	1.000000	0.333333	0.742857	1.000000	0.25	0.666667	0.178778	0.222222	0.071429	0.0	0.000000	0.0	0.000000	0.150	0.500000	1.000000	0.100	0.166667	0.066667	0.117647

1470 rows × 26 columns

xtrain,xtest,ytrain,ytest = train_test_split(df_norm,y,test_size=0.25,random_state=25)

knn_model = KNeighborsRegressor()
hyp_grid = {'n_neighbors':np.arange(1,40),'p':[1,2]}

gscv_knn_model = GridSearchCV(knn_model,hyp_grid,cv=5)
gscv_knn_model.fit(xtrain,ytrain)
gscv_knn_model.best_estimator_

KNeighborsRegressor(n_neighbors=37)

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knn_model = KNeighborsRegressor(n_neighbors=37, p=1)
knn_model.fit(xtrain,ytrain)

KNeighborsRegressor(n_neighbors=37, p=1)

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# Testing data evaluation
ypredtest = knn_model.predict(xtest)

MAE = mean_absolute_error(ytest,ypredtest)
print('MAE:',MAE)

MSE = mean_squared_error(ytest,ypredtest)
print('MSE:',MSE)

RMSE = np.sqrt(MSE)
print('RMSE',RMSE)

RS = r2_score(ytest,ypredtest)
print('RS:',RS)

ADJS = 1-((1-RS)*(len(xtest)-1)/(len(xtest)-len(x_lin.columns)-1))
print('ADJS:',ADJS)

MAE: 6245.193375440658
MSE: 51160933.41318441
RMSE 7152.687146323709
RS: -0.022467704993947013
ADJS: -0.10042711945096339

7.3] Decision Tree

xtrain,xtest,ytraon,ytest = train_test_split(df_lin_7,y,test_size=0.25,random_state=31)

# Model building
dt_reg = DecisionTreeRegressor()
dt_reg.fit(xtrain,ytrain)

DecisionTreeRegressor()

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# training data evaluation
ypredtrain = dt_reg.predict(xtrain)

mse = mean_squared_error(ytrain, ypredtrain)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

r2 = r2_score(ytrain, ypredtrain)
print("R2 :",r2)

MSE : 0.0
RMSE : 0.0
R2 : 1.0

# testing data evaluation
ypred = dt_reg.predict(xtest)

mse = mean_squared_error(ytest, ypred)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

r2 = r2_score(ytest, ypred)
print("R2 :",r2)

MSE : 96889957.37228261
RMSE : 9843.269648459429
R2 : -1.014599356565217

Problem Statements

8 From Classification Model Using Y = attrition and choose the best model logistic regression

import pandas as pd
import numpy as np

from sklearn.linear_model import LinearRegression, Lasso, Ridge, LogisticRegression
from sklearn.neighbors import KNeighborsRegressor,KNeighborsClassifier
from sklearn.tree import DecisionTreeRegressor, plot_tree
from sklearn.cluster import KMeans
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report,recall_score,f1_score,precision_score

import tensorflow as tf
import keras

import seaborn as sns
import matplotlib.pyplot as plt

import warnings
warnings.filterwarnings('ignore')

df_log = pd.read_csv('G:\ETLHive Pune\Project\HR-Data Atrition Project\HR-Employee-Attrition-Table 1.csv')
df_log.head()

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	Gender	HourlyRate	JobInvolvement	JobLevel	JobRole	JobSatisfaction	MaritalStatus	MonthlyIncome	MonthlyRate	NumCompaniesWorked	Over18	OverTime	PercentSalaryHike	PerformanceRating	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	2	Female	94	3	2	Sales Executive	4	Single	5993	19479	8	Y	Yes	11	3	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	3	Male	61	2	2	Research Scientist	2	Married	5130	24907	1	Y	No	23	4	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	4	Male	92	2	1	Laboratory Technician	3	Single	2090	2396	6	Y	Yes	15	3	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	4	Female	56	3	1	Research Scientist	3	Married	2909	23159	1	Y	Yes	11	3	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	1	Male	40	3	1	Laboratory Technician	2	Married	3468	16632	9	Y	No	12	3	4	80	1	6	3	3	2	2	2	2

import pandas as pd
from google.colab import files
uploaded = files.upload()
import io
df_log = pd.read_csv(io.BytesIO(uploaded['HR-Employee-Attrition-Table 1.csv']))
df_log.head()

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Saving HR-Employee-Attrition-Table 1.csv to HR-Employee-Attrition-Table 1 (2).csv

	Attrition	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	0	49	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	1	37	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	0	33	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	0	27	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2

5 rows × 35 columns

y_log = df_log['Attrition']

# Seprate Categorical and continious columns

cat = []
con = []
for i in df_log.columns:
    if (df_log[i].dtypes == 'object'):
        cat.append(i)
    else:
        con.append(i)

df_train_cat = cat
df_train_cat = df_log[cat]
df_train_cat

	BusinessTravel	Department	EducationField	Gender	JobRole	MaritalStatus	Over18	OverTime
0	Travel_Rarely	Sales	Life Sciences	Female	Sales Executive	Single	Y	Yes
1	Travel_Frequently	Research & Development	Life Sciences	Male	Research Scientist	Married	Y	No
2	Travel_Rarely	Research & Development	Other	Male	Laboratory Technician	Single	Y	Yes
3	Travel_Frequently	Research & Development	Life Sciences	Female	Research Scientist	Married	Y	Yes
4	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
...	...	...	...	...	...	...	...	...
1465	Travel_Frequently	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No
1466	Travel_Rarely	Research & Development	Medical	Male	Healthcare Representative	Married	Y	No
1467	Travel_Rarely	Research & Development	Life Sciences	Male	Manufacturing Director	Married	Y	Yes
1468	Travel_Frequently	Sales	Medical	Male	Sales Executive	Married	Y	No
1469	Travel_Rarely	Research & Development	Medical	Male	Laboratory Technician	Married	Y	No

1470 rows × 8 columns

df_train_con = con
df_train_con=df_log[con]
df_train_con

	Attrition	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	1	41	1102	1	2	1	1	2	94	3	...	1	80	0	8	0	1	6	4	0	5
1	0	49	279	8	1	1	2	3	61	2	...	4	80	1	10	3	3	10	7	1	7
2	1	37	1373	2	2	1	4	4	92	2	...	2	80	0	7	3	3	0	0	0	0
3	0	33	1392	3	4	1	5	4	56	3	...	3	80	0	8	3	3	8	7	3	0
4	0	27	591	2	1	1	7	1	40	3	...	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	36	884	23	2	1	2061	3	41	4	...	3	80	1	17	3	3	5	2	0	3
1466	0	39	613	6	1	1	2062	4	42	2	...	1	80	1	9	5	3	7	7	1	7
1467	0	27	155	4	3	1	2064	2	87	4	...	2	80	1	6	0	3	6	2	0	3
1468	0	49	1023	2	3	1	2065	4	63	2	...	4	80	0	17	3	2	9	6	0	8
1469	0	34	628	8	3	1	2068	2	82	4	...	1	80	0	6	3	4	4	3	1	2

1470 rows × 27 columns

# Removing Outliers

# Visualisation of continious columns

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train_con.columns):
    if df_train_con[i].dtypes == 'int64' or df_train_con[i].dtypes == 'float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con[i])

for i in df_train_con.columns:
    q1 = df_train_con[i].quantile(0.25)
    q3 = df_train_con[i].quantile(0.75)
    IQR = q3-q1
    uppertail = q3+1.5*IQR
    lowertail = q1-1.5*IQR
    df_train_con.loc[(df_train_con[i]>uppertail)|(df_train_con[i]<lowertail)]
    mean_1= df_train_con[i].mean()
    df_train_con.loc[(df_train_con[i]>uppertail)| (df_train_con[i]<lowertail),i]=mean_1

plt.figure(figsize=(15,19))
for x1,i in enumerate(df_train_con.columns):
    if df_train_con[i].dtypes=='int64' or df_train_con[i].dtypes=='float64':
        plt.subplot(9,3,x1+1)
        sns.boxplot(df_train_con[i])

# One hot Encoding
df_log_dum = pd.get_dummies(df_train_cat)

df_log_dum

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	BusinessTravel_Travel_Rarely	Department_Human Resources	Department_Research & Development	Department_Sales	EducationField_Human Resources	EducationField_Life Sciences	EducationField_Marketing	EducationField_Medical	...	JobRole_Research Director	JobRole_Research Scientist	JobRole_Sales Executive	JobRole_Sales Representative	MaritalStatus_Divorced	MaritalStatus_Married	MaritalStatus_Single	Over18_Y	OverTime_No	OverTime_Yes
0	0	0	1	0	0	1	0	1	0	0	...	0	0	1	0	0	0	1	1	0	1
1	0	1	0	0	1	0	0	1	0	0	...	0	1	0	0	0	1	0	1	1	0
2	0	0	1	0	1	0	0	0	0	0	...	0	0	0	0	0	0	1	1	0	1
3	0	1	0	0	1	0	0	1	0	0	...	0	1	0	0	0	1	0	1	0	1
4	0	0	1	0	1	0	0	0	0	1	...	0	0	0	0	0	1	0	1	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	0	0	1	0	0	0	0	1	...	0	0	0	0	0	1	0	1	1	0
1466	0	0	1	0	1	0	0	0	0	1	...	0	0	0	0	0	1	0	1	1	0
1467	0	0	1	0	1	0	0	1	0	0	...	0	0	0	0	0	1	0	1	0	1
1468	0	1	0	0	0	1	0	0	0	1	...	0	0	1	0	0	1	0	1	1	0
1469	0	0	1	0	1	0	0	0	0	1	...	0	0	0	0	0	1	0	1	1	0

1470 rows × 29 columns

# Standardisation

std_scaler = StandardScaler()
std_scaler1 = std_scaler.fit_transform(df_train_con)

x_log = pd.DataFrame(std_scaler1,columns=df_train_con.columns)

x_log.shape

(1470, 27)

df_final_log = pd.concat([df_log_dum,x_log],axis=1)

df_final_log.columns

Index(['BusinessTravel_Non-Travel', 'BusinessTravel_Travel_Frequently',
       'BusinessTravel_Travel_Rarely', 'Department_Human Resources',
       'Department_Research & Development', 'Department_Sales',
       'EducationField_Human Resources', 'EducationField_Life Sciences',
       'EducationField_Marketing', 'EducationField_Medical',
       'EducationField_Other', 'EducationField_Technical Degree',
       'Gender_Female', 'Gender_Male', 'JobRole_Healthcare Representative',
       'JobRole_Human Resources', 'JobRole_Laboratory Technician',
       'JobRole_Manager', 'JobRole_Manufacturing Director',
       'JobRole_Research Director', 'JobRole_Research Scientist',
       'JobRole_Sales Executive', 'JobRole_Sales Representative',
       'MaritalStatus_Divorced', 'MaritalStatus_Married',
       'MaritalStatus_Single', 'Over18_Y', 'OverTime_No', 'OverTime_Yes',
       'Attrition', 'Age', 'DailyRate', 'DistanceFromHome', 'Education',
       'EmployeeCount', 'EmployeeNumber', 'EnvironmentSatisfaction',
       'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobSatisfaction',
       'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',
       'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction',
       'StandardHours', 'StockOptionLevel', 'TotalWorkingYears',
       'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany',
       'YearsInCurrentRole', 'YearsSinceLastPromotion',
       'YearsWithCurrManager'],
      dtype='object')

# Checking Linearity

noncorrelation = [column for column in df_final_log if abs(df_final_log[column].corr(df_final_log['Attrition']))<0.05]
corr_matrix = df_final_log.drop(noncorrelation,axis=1)
corr_matrix.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
0	0	0	0	1	0	0	0	0	0	0	...	-0.057788	1.153254	0.129018	0.0	-1.018674	-0.374906	-2.493820	0.041137	-0.018341	0.294570
1	0	1	1	0	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.140791	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	0.888852
2	0	0	1	0	0	0	0	1	0	0	...	-0.961486	0.246200	-1.091220	0.0	-1.018674	-0.533426	0.338096	-1.501837	-1.219103	-1.191138
3	0	1	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.835167	0.0	-1.018674	-0.374906	0.338096	0.555462	0.882230	-1.191138
4	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.660400	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-0.596855

5 rows × 35 columns

# Check Multicolinearity

log7 = corr_matrix.drop('Attrition',axis=1)

from statsmodels.stats.outliers_influence import variance_inflation_factor
vif_log = pd.DataFrame()
vif_log['Features'] =log7.columns
vif_log['VIF'] = [variance_inflation_factor(log7.values,i) for i in range(log7.shape[1])]
vif_log

	Features	VIF
0	BusinessTravel_Non-Travel	1.046263
1	BusinessTravel_Travel_Frequently	1.042999
2	Department_Research & Development	6.451867
3	Department_Sales	6.756041
4	EducationField_Marketing	1.433720
5	EducationField_Technical Degree	1.030463
6	JobRole_Healthcare Representative	1.765789
7	JobRole_Laboratory Technician	1.517876
8	JobRole_Manager	2.566583
9	JobRole_Manufacturing Director	1.823432
10	JobRole_Research Director	2.516362
11	JobRole_Sales Representative	1.507687
12	MaritalStatus_Divorced	inf
13	MaritalStatus_Married	inf
14	MaritalStatus_Single	inf
15	Over18_Y	0.000000
16	OverTime_No	inf
17	OverTime_Yes	inf
18	Age	1.607862
19	DailyRate	1.025251
20	DistanceFromHome	1.019110
21	EmployeeCount	NaN
22	EnvironmentSatisfaction	1.028213
23	JobInvolvement	1.017833
24	JobLevel	6.005247
25	JobSatisfaction	1.015843
26	MonthlyIncome	2.618679
27	StandardHours	NaN
28	StockOptionLevel	2.128814
29	TotalWorkingYears	2.575361
30	WorkLifeBalance	1.021160
31	YearsAtCompany	4.451725
32	YearsInCurrentRole	3.287478
33	YearsWithCurrManager	2.866381

featurestodrop = vif_log.loc[vif_log['VIF']>10]
droplist = featurestodrop['Features']
droplist = list(droplist)
len(droplist)
print(droplist)

['MaritalStatus_Divorced', 'MaritalStatus_Married', 'MaritalStatus_Single', 'OverTime_No', 'OverTime_Yes']

final_log=log7.drop(droplist,axis=1)

final_log

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
0	0	0	0	1	0	0	0	0	0	0	...	-0.057788	1.153254	0.129018	0.0	-1.018674	-0.374906	-2.493820	0.041137	-0.018341	0.294570
1	0	1	1	0	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.140791	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	0.888852
2	0	0	1	0	0	0	0	1	0	0	...	-0.961486	0.246200	-1.091220	0.0	-1.018674	-0.533426	0.338096	-1.501837	-1.219103	-1.191138
3	0	1	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.835167	0.0	-1.018674	-0.374906	0.338096	0.555462	0.882230	-1.191138
4	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.660400	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-0.596855
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	1	0	0	0	0	1	0	0	...	-0.057788	1.153254	-0.940839	0.0	0.510149	1.051772	0.338096	-0.216025	-0.618722	-0.299713
1466	0	0	1	0	0	0	1	0	0	0	...	0.845911	-1.567907	1.378958	0.0	0.510149	-0.216386	0.338096	0.298300	0.882230	0.888852
1467	0	0	1	0	0	0	0	0	0	1	...	-0.057788	-0.660853	0.175602	0.0	0.510149	-0.691946	0.338096	0.041137	-0.618722	-0.299713
1468	0	1	0	1	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.059504	0.0	-1.018674	1.051772	-1.077862	0.812625	0.582040	1.185994
1469	0	0	1	0	0	0	0	1	0	0	...	-0.057788	0.246200	-0.367768	0.0	-1.018674	-0.691946	1.754054	-0.473188	-0.318532	-0.596855

1470 rows × 29 columns

# Spliiting

xtrain,xtest,ytrain,ytest = train_test_split(final_log,y_log,test_size=0.25,random_state=31,stratify=y_log)

xtrain

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
1069	0	0	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-1.255982	0.0	0.510149	-1.484545	-2.493820	-1.244675	-1.219103	-1.191138
841	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.620070	0.0	-1.018674	-0.691946	0.338096	-0.473188	-0.318532	-0.596855
332	0	1	1	0	0	0	0	0	0	0	...	-0.057788	0.246200	-0.222390	0.0	-1.018674	1.527331	-1.077862	-0.473188	-0.318532	-0.299713
1337	0	0	0	1	0	0	0	0	0	0	...	-0.961486	-0.660853	-0.851737	0.0	0.510149	-1.484545	0.338096	-1.244675	-1.219103	-1.191138
292	0	1	0	1	1	0	0	0	0	0	...	-0.961486	-0.660853	-0.872684	0.0	0.510149	-1.326025	-1.077862	-0.987512	-0.618722	-0.596855
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
48	0	1	0	1	1	0	0	0	0	0	...	-0.057788	1.153254	0.059925	0.0	-1.018674	0.576212	0.338096	0.812625	0.582040	1.185994
220	0	0	1	0	0	0	0	1	0	0	...	-0.057788	-0.660853	0.104320	0.0	-1.018674	0.893252	1.754054	1.841275	2.082992	0.888852
551	0	0	0	0	0	0	0	0	0	0	...	-0.057788	-0.660853	0.252824	0.0	0.510149	0.259173	-2.493820	0.555462	-0.318532	0.591711
1435	0	0	1	0	0	0	0	0	0	0	...	-0.961486	1.153254	-0.983046	0.0	-1.018674	-0.691946	0.338096	-0.473188	-0.318532	-0.596855
1379	0	1	0	0	0	0	0	0	0	0	...	-0.961486	-0.660853	-0.849548	0.0	-1.018674	-1.484545	0.338096	-1.244675	-1.219103	-1.191138

1102 rows × 29 columns

xtest

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
302	0	0	1	0	0	0	1	0	0	0	...	-0.057788	-1.567907	0.025222	0.0	-1.018674	-0.216386	0.338096	0.555462	-0.318532	0.888852
200	0	1	1	0	0	1	0	0	0	1	...	-0.057788	-1.567907	-0.400908	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-1.191138
944	1	0	1	0	0	0	0	1	0	0	...	-0.057788	1.153254	0.341927	0.0	0.195024	-0.057867	0.338096	0.812625	1.182421	0.294570
929	0	1	1	0	0	0	0	1	0	0	...	-0.961486	1.153254	-0.535657	0.0	0.510149	-1.326025	0.338096	-0.987512	-0.618722	-0.596855
1047	0	1	0	1	0	0	0	0	0	0	...	-0.057788	-1.567907	-0.377460	0.0	2.038972	-0.850466	0.338096	-0.473188	-0.318532	-0.299713
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1310	0	1	1	0	0	0	0	0	0	0	...	1.749610	0.246200	3.191026	0.0	-1.018674	2.002890	0.338096	-0.987512	-0.618722	-0.596855
766	0	0	1	0	0	0	0	0	0	0	...	2.653309	0.246200	0.288444	0.0	0.510149	0.144974	-1.077862	0.555462	-0.918912	0.888852
1233	0	0	1	0	0	0	0	0	0	0	...	-0.961486	1.153254	-0.849861	0.0	0.510149	-0.057867	-1.077862	1.069787	-1.219103	1.185994
504	0	1	0	1	0	0	0	0	0	0	...	-0.057788	-1.567907	-0.404660	0.0	2.038972	-0.850466	0.338096	-1.244675	-0.918912	-1.191138
362	1	0	0	1	0	0	0	0	0	0	...	-0.961486	1.153254	-0.928646	0.0	-1.018674	-1.167505	-1.077862	-0.730350	-0.618722	-0.596855

368 rows × 29 columns

ytrain

1069    0
841     0
332     0
1337    0
292     0
       ..
48      0
220     0
551     0
1435    0
1379    1
Name: Attrition, Length: 1102, dtype: int64

ytest

302     0
200     0
944     0
929     0
1047    0
       ..
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Name: Attrition, Length: 368, dtype: int64

a) Logistic Regression

# Model Evalution

log_model = LogisticRegression()
log_model.fit(xtrain,ytrain)

LogisticRegression()

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#  Testing data evaluation
ypredtest = log_model.predict(xtest)

Accuracy = accuracy_score(ytest,ypredtest)
print('Accuracy:',Accuracy)

confusionmatrix = confusion_matrix(ytest,ypredtest)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytest,ypredtest)
print('Precision:',Precision)

Recall = recall_score(ytest,ypredtest)
print('Recall:',Recall)

F1score = f1_score(ytest,ypredtest)
print('F1score:',F1score)

Classification_report = classification_report(ytest,ypredtest)
print('Classification report:\n',Classification_report)

Accuracy: 0.8586956521739131
Confusion matrix: 
 [[303   6]
 [ 46  13]]
Precision: 0.6842105263157895
Recall: 0.22033898305084745
F1score: 0.3333333333333333
Classification report:
               precision    recall  f1-score   support

           0       0.87      0.98      0.92       309
           1       0.68      0.22      0.33        59

    accuracy                           0.86       368
   macro avg       0.78      0.60      0.63       368
weighted avg       0.84      0.86      0.83       368

# Training data evaluation
ypredtrain = log_model.predict(xtrain)

Accuracy = accuracy_score(ytrain,ypredtrain)
print('Accuracy:',Accuracy)

confusionmatrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytrain,ypredtrain)
print('Precision:',Precision)

Recall = recall_score(ytrain,ypredtrain)
print('Recall:',Recall)

F1score = f1_score(ytrain,ypredtrain)
print('F1score:',F1score)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification report:\n',Classification_report)

Accuracy: 0.8693284936479129
Confusion matrix: 
 [[912  12]
 [132  46]]
Precision: 0.7931034482758621
Recall: 0.25842696629213485
F1score: 0.38983050847457634
Classification report:
               precision    recall  f1-score   support

           0       0.87      0.99      0.93       924
           1       0.79      0.26      0.39       178

    accuracy                           0.87      1102
   macro avg       0.83      0.62      0.66      1102
weighted avg       0.86      0.87      0.84      1102

b] KNN

Knn_model = KNeighborsClassifier()
Knn_model.fit(xtrain,ytrain)

KNeighborsClassifier()

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# Testing data evaluation
ypredtest = Knn_model.predict(xtest)

Accuracy = accuracy_score(ytest,ypredtest)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report:\n',Classification_report)

Accuracy: 0.8396739130434783
Confusion_matrix:
 [[300   9]
 [ 50   9]]
Classification_report:
               precision    recall  f1-score   support

           0       0.86      0.97      0.91       309
           1       0.50      0.15      0.23        59

    accuracy                           0.84       368
   macro avg       0.68      0.56      0.57       368
weighted avg       0.80      0.84      0.80       368

# Training data evaluation
ypredtrain = Knn_model.predict(xtrain)

Accuracy = accuracy_score(ytrain,ypredtrain)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix:\n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report:\n',Classification_report)

Accuracy: 0.8702359346642469
Confusion_matrix:
 [[911  13]
 [130  48]]
Classification_report:
               precision    recall  f1-score   support

           0       0.88      0.99      0.93       924
           1       0.79      0.27      0.40       178

    accuracy                           0.87      1102
   macro avg       0.83      0.63      0.66      1102
weighted avg       0.86      0.87      0.84      1102

c] Naive Bayes Algorithm

from sklearn.naive_bayes import BernoulliNB

naive_model = BernoulliNB(binarize=True)
naive_model.fit(xtrain,ytrain)

BernoulliNB(binarize=True)

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# Training data evaluation

ypredtrain = naive_model.predict(xtrain)

Accuracy = accuracy_score(ytrain,ypredtrain)
print('Accuracy:',Accuracy)

confusionmatrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytrain,ypredtrain)
print('Precision:',Precision)

Recall = recall_score(ytrain,ypredtrain)
print('Recall:',Recall)

F1score = f1_score(ytrain,ypredtrain)
print('F1score:',F1score)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification report:\n',Classification_report)

Accuracy: 0.838475499092559
Confusion matrix: 
 [[924   0]
 [178   0]]
Precision: 0.0
Recall: 0.0
F1score: 0.0
Classification report:
               precision    recall  f1-score   support

           0       0.84      1.00      0.91       924
           1       0.00      0.00      0.00       178

    accuracy                           0.84      1102
   macro avg       0.42      0.50      0.46      1102
weighted avg       0.70      0.84      0.76      1102

# Testing data evaluation

ypredtest = naive_model.predict(xtest)

Accuracy = accuracy_score(ytest,ypredtest)
print('Accuracy:',Accuracy)

confusionmatrix = confusion_matrix(ytest,ypredtest)
print('Confusion matrix: \n',confusionmatrix)

Precision = precision_score(ytest,ypredtest)
print('Precision:',Precision)

Recall = recall_score(ytest,ypredtest)
print('Recall:',Recall)

F1score = f1_score(ytest,ypredtest)
print('F1score:',F1score)

Classification_report = classification_report(ytest,ypredtest)
print('Classification report:\n',Classification_report)

Accuracy: 0.8396739130434783
Confusion matrix: 
 [[309   0]
 [ 59   0]]
Precision: 0.0
Recall: 0.0
F1score: 0.0
Classification report:
               precision    recall  f1-score   support

           0       0.84      1.00      0.91       309
           1       0.00      0.00      0.00        59

    accuracy                           0.84       368
   macro avg       0.42      0.50      0.46       368
weighted avg       0.71      0.84      0.77       368

d] Decision tree

# Model Training

dt_reg = DecisionTreeRegressor()
dt_reg.fit(xtrain, ytrain)

DecisionTreeRegressor()

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# Training data evaluation

ypredtrain = dt_reg.predict(xtrain)

mse = mean_squared_error(ytrain, ypredtrain)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

r2 = r2_score(ytrain, ypredtrain)
print("R2 :",r2)

MSE : 0.0
RMSE : 0.0
R2 : 1.0

# Testing data Evaluation

ypred = dt_reg.predict(xtest)

mse = mean_squared_error(ytest, ypred)
print("MSE :",mse)

rmse = np.sqrt(mse)
print("RMSE :",rmse)

r2 = r2_score(ytest, ypred)
print("R2 :",r2)

MSE : 0.28532608695652173
RMSE : 0.5341592337089398
R2 : -1.119466842191871

e] SVM

# Model Training

svc_model = SVC()
svc_model.fit(xtrain,ytrain)

SVC()

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# Testing data evaluation
ypredtest = svc_model.predict(xtest)


Accuracy = accuracy_score(ytest,ypredtest)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Accuracy: 0.8478260869565217
Confusion_matrix: 
 [[307   2]
 [ 54   5]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.85      0.99      0.92       309
           1       0.71      0.08      0.15        59

    accuracy                           0.85       368
   macro avg       0.78      0.54      0.53       368
weighted avg       0.83      0.85      0.79       368

# Training data evaluation
ypredtrain = svc_model.predict(xtrain)

Accuracy = accuracy_score(ytrain,ypredtrain)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Accuracy: 0.8720508166969148
Confusion_matrix: 
 [[921   3]
 [138  40]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.87      1.00      0.93       924
           1       0.93      0.22      0.36       178

    accuracy                           0.87      1102
   macro avg       0.90      0.61      0.65      1102
weighted avg       0.88      0.87      0.84      1102

# Hyperparameter tuning

svc_model = SVC()

hyp_grid = {'C':np.arange(0,50),
            'kernel' : ['linear', 'poly', 'rbf', 'sigmoid']}

gscv_scv_model = GridSearchCV(svc_model,hyp_grid,cv=5)

gscv_scv_model.fit(xtrain,ytrain)

GridSearchCV(cv=5, estimator=SVC(),
             param_grid={'C': array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),
                         'kernel': ['linear', 'poly', 'rbf', 'sigmoid']})

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gscv_scv_model.best_estimator_

SVC(C=1, kernel='poly')

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svc_model = SVC(C=1, kernel='linear')
svc_model.fit(xtrain,ytrain)

SVC(C=1, kernel='linear')

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# Testing data evaluation
ypredtest = svc_model.predict(xtest)


Accuracy = accuracy_score(ytest,ypredtest)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytest,ypredtest)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytest,ypredtest)
print('Classification_report: \n',Classification_report)

Accuracy: 0.8396739130434783
Confusion_matrix: 
 [[309   0]
 [ 59   0]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.84      1.00      0.91       309
           1       0.00      0.00      0.00        59

    accuracy                           0.84       368
   macro avg       0.42      0.50      0.46       368
weighted avg       0.71      0.84      0.77       368

# Training data evaluation
ypredtrain = svc_model.predict(xtrain)

Accuracy = accuracy_score(ytrain,ypredtrain)
print('Accuracy:',Accuracy)

Confusion_matrix = confusion_matrix(ytrain,ypredtrain)
print('Confusion_matrix: \n',Confusion_matrix)

Classification_report = classification_report(ytrain,ypredtrain)
print('Classification_report: \n',Classification_report)

Accuracy: 0.838475499092559
Confusion_matrix: 
 [[924   0]
 [178   0]]
Classification_report: 
               precision    recall  f1-score   support

           0       0.84      1.00      0.91       924
           1       0.00      0.00      0.00       178

    accuracy                           0.84      1102
   macro avg       0.42      0.50      0.46      1102
weighted avg       0.70      0.84      0.76      1102

f] ANN

# Model Building

import tensorflow as tf
import keras

from keras import Sequential
from keras.layers import Dense,Dropout

nn = Sequential()
 # Create input layer and 1st Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform',input_dim=(29)))  

# Add 1st Dropout layer
nn.add(Dropout(rate=0.5))

# Add 2nd Hidden layer
nn.add(Dense(units = 6,activation='relu',kernel_initializer='he_uniform')) 

# Add 2nd Dropout layer
nn.add(Dropout(rate=0.5))

# Add output layer
nn.add(Dense(units=1 ,activation='sigmoid', kernel_initializer='glorot_uniform'))

# Compile
nn.compile(optimizer='Adam',loss='binary_crossentropy' ,metrics=['accuracy','Recall','Precision'])

# Training
model_train= nn.fit(xtrain,ytrain,validation_split=0.2,epochs=100,batch_size=20)

Epoch 1/100
45/45 [==============================] - 2s 10ms/step - loss: 0.7820 - accuracy: 0.7242 - recall: 0.1942 - precision: 0.1709 - val_loss: 0.6188 - val_accuracy: 0.7602 - val_recall: 0.2821 - val_precision: 0.3056
Epoch 2/100
45/45 [==============================] - 0s 3ms/step - loss: 0.6897 - accuracy: 0.7582 - recall: 0.1655 - precision: 0.1917 - val_loss: 0.5879 - val_accuracy: 0.8416 - val_recall: 0.2051 - val_precision: 0.6667
Epoch 3/100
45/45 [==============================] - 0s 3ms/step - loss: 0.6642 - accuracy: 0.7696 - recall: 0.1295 - precision: 0.1800 - val_loss: 0.5646 - val_accuracy: 0.8235 - val_recall: 0.0256 - val_precision: 0.5000
Epoch 4/100
45/45 [==============================] - 0s 3ms/step - loss: 0.6437 - accuracy: 0.7877 - recall: 0.1511 - precision: 0.2333 - val_loss: 0.5488 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 5/100
45/45 [==============================] - 0s 3ms/step - loss: 0.5764 - accuracy: 0.8036 - recall: 0.1007 - precision: 0.2258 - val_loss: 0.5312 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 6/100
45/45 [==============================] - 0s 3ms/step - loss: 0.5815 - accuracy: 0.8184 - recall: 0.1007 - precision: 0.2857 - val_loss: 0.5173 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 7/100
45/45 [==============================] - 0s 3ms/step - loss: 0.5608 - accuracy: 0.8150 - recall: 0.0719 - precision: 0.2273 - val_loss: 0.5092 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 8/100
45/45 [==============================] - 0s 5ms/step - loss: 0.5603 - accuracy: 0.8070 - recall: 0.0432 - precision: 0.1395 - val_loss: 0.5015 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 9/100
45/45 [==============================] - 0s 4ms/step - loss: 0.5463 - accuracy: 0.8297 - recall: 0.0288 - precision: 0.2105 - val_loss: 0.4936 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 10/100
45/45 [==============================] - 0s 4ms/step - loss: 0.5165 - accuracy: 0.8343 - recall: 0.0576 - precision: 0.3478 - val_loss: 0.4856 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 11/100
45/45 [==============================] - 0s 4ms/step - loss: 0.5143 - accuracy: 0.8365 - recall: 0.0360 - precision: 0.3333 - val_loss: 0.4780 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 12/100
45/45 [==============================] - 0s 4ms/step - loss: 0.5000 - accuracy: 0.8388 - recall: 0.0288 - precision: 0.3636 - val_loss: 0.4719 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 13/100
45/45 [==============================] - 0s 5ms/step - loss: 0.4635 - accuracy: 0.8388 - recall: 0.0144 - precision: 0.2857 - val_loss: 0.4664 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 14/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4849 - accuracy: 0.8331 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4618 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 15/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4845 - accuracy: 0.8343 - recall: 0.0072 - precision: 0.1111 - val_loss: 0.4566 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 16/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4765 - accuracy: 0.8388 - recall: 0.0144 - precision: 0.2857 - val_loss: 0.4528 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 17/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4894 - accuracy: 0.8365 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4501 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 18/100
45/45 [==============================] - 0s 5ms/step - loss: 0.4667 - accuracy: 0.8365 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4474 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 19/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4559 - accuracy: 0.8400 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4430 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 20/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4641 - accuracy: 0.8411 - recall: 0.0072 - precision: 0.3333 - val_loss: 0.4423 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 21/100
45/45 [==============================] - 0s 5ms/step - loss: 0.4657 - accuracy: 0.8422 - recall: 0.0072 - precision: 0.5000 - val_loss: 0.4409 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 22/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4454 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4398 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 23/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4594 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4381 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 24/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4510 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4368 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 25/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4536 - accuracy: 0.8434 - recall: 0.0072 - precision: 1.0000 - val_loss: 0.4362 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 26/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4648 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4358 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 27/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4404 - accuracy: 0.8388 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4349 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 28/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4421 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4347 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 29/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4382 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4339 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 30/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4371 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4335 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 31/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4403 - accuracy: 0.8422 - recall: 0.0072 - precision: 0.5000 - val_loss: 0.4344 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 32/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4349 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4335 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 33/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4197 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4315 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 34/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4419 - accuracy: 0.8400 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4314 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 35/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4475 - accuracy: 0.8422 - recall: 0.0072 - precision: 0.5000 - val_loss: 0.4328 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 36/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4359 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4329 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 37/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4391 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4339 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 38/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4329 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4330 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 39/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4217 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4327 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 40/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4311 - accuracy: 0.8434 - recall: 0.0072 - precision: 1.0000 - val_loss: 0.4334 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 41/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4287 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4323 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 42/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4163 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4312 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 43/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4315 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4312 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 44/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4261 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4308 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 45/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4284 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4306 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 46/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4213 - accuracy: 0.8400 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4310 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 47/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4313 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4321 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 48/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4386 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4323 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 49/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4193 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4332 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 50/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4189 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4317 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 51/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4091 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4324 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 52/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4156 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4322 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 53/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4353 - accuracy: 0.8411 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4330 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 54/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4202 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4346 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 55/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4289 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4356 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 56/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4106 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4363 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 57/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4206 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4371 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 58/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4136 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4371 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 59/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4102 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4371 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 60/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4167 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4374 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 61/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4187 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4380 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 62/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4080 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4387 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 63/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4085 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4401 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 64/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4196 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4384 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 65/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4223 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4379 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 66/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4198 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4371 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 67/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4205 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4380 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 68/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4081 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4371 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 69/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4197 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4375 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 70/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4095 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4383 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 71/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4032 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4383 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 72/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4121 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4374 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 73/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4096 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4377 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 74/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4055 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4402 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 75/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4002 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4416 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 76/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4028 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4400 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 77/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4061 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4389 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 78/100
45/45 [==============================] - 0s 3ms/step - loss: 0.3982 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4400 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 79/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4066 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4419 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 80/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4022 - accuracy: 0.8422 - recall: 0.0000e+00 - precision: 0.0000e+00 - val_loss: 0.4416 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 81/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4022 - accuracy: 0.8434 - recall: 0.0072 - precision: 1.0000 - val_loss: 0.4401 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 82/100
45/45 [==============================] - 0s 3ms/step - loss: 0.3980 - accuracy: 0.8445 - recall: 0.0144 - precision: 1.0000 - val_loss: 0.4398 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 83/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4137 - accuracy: 0.8434 - recall: 0.0072 - precision: 1.0000 - val_loss: 0.4415 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 84/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4042 - accuracy: 0.8411 - recall: 0.0072 - precision: 0.3333 - val_loss: 0.4415 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 85/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3928 - accuracy: 0.8434 - recall: 0.0072 - precision: 1.0000 - val_loss: 0.4411 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 86/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4048 - accuracy: 0.8422 - recall: 0.0072 - precision: 0.5000 - val_loss: 0.4424 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 87/100
45/45 [==============================] - 0s 3ms/step - loss: 0.4015 - accuracy: 0.8400 - recall: 0.0072 - precision: 0.2500 - val_loss: 0.4437 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 88/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3983 - accuracy: 0.8445 - recall: 0.0144 - precision: 1.0000 - val_loss: 0.4448 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 89/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3965 - accuracy: 0.8502 - recall: 0.0504 - precision: 1.0000 - val_loss: 0.4444 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 90/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3985 - accuracy: 0.8479 - recall: 0.0576 - precision: 0.7273 - val_loss: 0.4441 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 91/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3975 - accuracy: 0.8445 - recall: 0.0432 - precision: 0.6000 - val_loss: 0.4457 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 92/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3998 - accuracy: 0.8445 - recall: 0.0360 - precision: 0.6250 - val_loss: 0.4469 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 93/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3867 - accuracy: 0.8468 - recall: 0.0288 - precision: 1.0000 - val_loss: 0.4471 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 94/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3866 - accuracy: 0.8468 - recall: 0.0432 - precision: 0.7500 - val_loss: 0.4496 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 95/100
45/45 [==============================] - 0s 4ms/step - loss: 0.3983 - accuracy: 0.8468 - recall: 0.0432 - precision: 0.7500 - val_loss: 0.4508 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 96/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3904 - accuracy: 0.8422 - recall: 0.0288 - precision: 0.5000 - val_loss: 0.4485 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 97/100
45/45 [==============================] - 0s 4ms/step - loss: 0.4032 - accuracy: 0.8411 - recall: 0.0360 - precision: 0.4545 - val_loss: 0.4491 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 98/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3996 - accuracy: 0.8422 - recall: 0.0288 - precision: 0.5000 - val_loss: 0.4503 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 99/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3903 - accuracy: 0.8456 - recall: 0.0288 - precision: 0.8000 - val_loss: 0.4498 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00
Epoch 100/100
45/45 [==============================] - 0s 5ms/step - loss: 0.3906 - accuracy: 0.8434 - recall: 0.0360 - precision: 0.5556 - val_loss: 0.4499 - val_accuracy: 0.8235 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00

# Model Testing

ypredtest = nn.predict(xtest)

12/12 [==============================] - 0s 1ms/step

ypredtest

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       [0.11566301],
       [0.17864135],
       [0.17864135],
       [0.2664947 ],
       [0.08195227],
       [0.11174984],
       [0.01494817],
       [0.20882936],
       [0.15095532],
       [0.00298889],
       [0.18600288],
       [0.08491654],
       [0.20261735],
       [0.14526552],
       [0.17864135],
       [0.21982045],
       [0.2440781 ],
       [0.26029631],
       [0.18553853],
       [0.21339819],
       [0.28174663],
       [0.23102461],
       [0.26029631],
       [0.22908273],
       [0.26029631],
       [0.19858061],
       [0.1377052 ],
       [0.03284998],
       [0.26029631],
       [0.1392803 ],
       [0.17864135],
       [0.20481087],
       [0.17864135],
       [0.27247092],
       [0.24391419],
       [0.01871445],
       [0.13344584],
       [0.09237079],
       [0.00121117],
       [0.08360118],
       [0.00102865],
       [0.16539492],
       [0.27777648],
       [0.25430283],
       [0.0637172 ],
       [0.17864135],
       [0.03634345],
       [0.26332825],
       [0.27286452],
       [0.30843747],
       [0.08077957],
       [0.26658297],
       [0.17102545],
       [0.15640703],
       [0.17864135],
       [0.13594404],
       [0.05219484],
       [0.00595472],
       [0.0783316 ],
       [0.17864135],
       [0.2593516 ],
       [0.16748501],
       [0.06541692],
       [0.23679696],
       [0.00642795],
       [0.2756765 ],
       [0.0244295 ],
       [0.03046309],
       [0.17864135],
       [0.06599958],
       [0.1735468 ],
       [0.26029631],
       [0.01298711],
       [0.17864135],
       [0.14433551],
       [0.22224829],
       [0.26916125],
       [0.01386248],
       [0.0545248 ],
       [0.10220438],
       [0.28250805],
       [0.1060359 ],
       [0.17695737],
       [0.10343977],
       [0.17864135],
       [0.05588656],
       [0.2539565 ],
       [0.03529732],
       [0.02105915],
       [0.1310736 ],
       [0.16897002],
       [0.17864135],
       [0.16364472],
       [0.08125693],
       [0.16763338],
       [0.17864135],
       [0.15711759],
       [0.19832133],
       [0.08390985],
       [0.23529173],
       [0.03127239],
       [0.17864135],
       [0.26029631],
       [0.22231479],
       [0.17864135],
       [0.21266054],
       [0.23025179],
       [0.18405075],
       [0.16127308],
       [0.16364063],
       [0.13972007],
       [0.01925687],
       [0.27160215],
       [0.30919692],
       [0.00588664],
       [0.01169947],
       [0.09484629],
       [0.26029631],
       [0.17864135],
       [0.14511535],
       [0.18220787],
       [0.0388039 ],
       [0.17864135],
       [0.09844014],
       [0.04057379],
       [0.1337107 ],
       [0.0125258 ],
       [0.16890198],
       [0.29679382],
       [0.09167103],
       [0.02465587],
       [0.17864135],
       [0.05458304],
       [0.08702319],
       [0.03677688],
       [0.17675959],
       [0.00092281],
       [0.0072814 ],
       [0.14503984],
       [0.10235583],
       [0.17864135],
       [0.01230869],
       [0.23082604],
       [0.00960664],
       [0.11529443],
       [0.03483866],
       [0.15413877],
       [0.07259345],
       [0.17864135],
       [0.17864135],
       [0.14462319],
       [0.17057335],
       [0.17864135],
       [0.15546933],
       [0.14479683],
       [0.17864135],
       [0.15372086],
       [0.06519894],
       [0.17864135],
       [0.0192605 ],
       [0.04998954],
       [0.09350831],
       [0.120751  ],
       [0.17354558],
       [0.07330868],
       [0.07280812],
       [0.17864135],
       [0.09785005],
       [0.17864135],
       [0.10088717],
       [0.05949574],
       [0.15908647],
       [0.1254853 ],
       [0.14708665],
       [0.0614772 ],
       [0.13817297],
       [0.02034603],
       [0.1802322 ],
       [0.2787524 ],
       [0.29827794],
       [0.16202964],
       [0.03965331],
       [0.24010457],
       [0.07203472],
       [0.01714903],
       [0.11134897],
       [0.27343938],
       [0.00378489],
       [0.03697024],
       [0.02776049],
       [0.06400913],
       [0.19636442],
       [0.14231376],
       [0.12078112],
       [0.1963701 ],
       [0.02792522],
       [0.17864135],
       [0.0022663 ],
       [0.23789994],
       [0.17864135],
       [0.24822074]], dtype=float32)

ypredtest = list(map(int,(ypredtest>0.4)))

print(ypredtest)

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

Accuracy = accuracy_score(ytest,ypredtest)
Accuracy

0.8396739130434783

final_y = pd.DataFrame(ypredtest)

final_pred = final_y.replace({1:'Y',0:'N'})
final_pred

	0
0	N
1	N
2	N
3	N
4	N
...	...
363	N
364	N
365	N
366	N
367	N

368 rows × 1 columns

#  choose prediction error and prediction of best model is logistic Model 
#  accuracy = 0.8586

Final Prediction

ypredtest = log_model.predict(xtest)

ypredtest

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Final_pred = pd.DataFrame(ypredtest)

Final_pred

	0
0	0
1	0
2	0
3	0
4	0
...	...
363	0
364	0
365	0
366	0
367	0

368 rows × 1 columns

Problem Statements

# 9. Clustering - Find intersting clusters using K-Means, Heirarchical and DBSCAN clustering.
# Connect to Domain scenario and its usefulness in analysis (Ignore Attrition column)

a] Grouping of Employees

final_log.head()

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
0	0	0	0	1	0	0	0	0	0	0	...	-0.057788	1.153254	0.129018	0.0	-1.018674	-0.374906	-2.493820	0.041137	-0.018341	0.294570
1	0	1	1	0	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.140791	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	0.888852
2	0	0	1	0	0	0	0	1	0	0	...	-0.961486	0.246200	-1.091220	0.0	-1.018674	-0.533426	0.338096	-1.501837	-1.219103	-1.191138
3	0	1	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.835167	0.0	-1.018674	-0.374906	0.338096	0.555462	0.882230	-1.191138
4	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.660400	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-0.596855

5 rows × 29 columns

1. kmeans Clustering

# Model Building

kmeans_model = KMeans(n_clusters=5)  # n_clusters=8 by default
kmeans_model.fit(x_log)

KMeans(n_clusters=5)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

# With cluster some of squares

kmeans_model.inertia_

28488.260625226452

# Labeling Variable

C_var = kmeans_model.fit_predict(x_log)
C_var

array([3, 4, 3, ..., 4, 2, 0], dtype=int32)

# Cluster = 0

index_0 = np.where(C_var==0)
index_0

(array([   3,    4,   10,   12,   13,   16,   17,   19,   20,   23,   30,
          31,   32,   35,   37,   38,   39,   40,   41,   49,   53,   54,
          57,   66,   68,   72,   74,   81,   84,   86,   97,   99,  101,
         104,  108,  109,  113,  114,  115,  125,  128,  130,  138,  141,
         143,  145,  146,  149,  156,  159,  161,  162,  164,  170,  174,
         175,  176,  179,  181,  183,  184,  191,  193,  196,  197,  198,
         200,  202,  203,  206,  207,  208,  224,  225,  230,  232,  238,
         240,  241,  242,  246,  248,  249,  252,  253,  254,  255,  258,
         262,  265,  267,  273,  274,  284,  287,  292,  294,  298,  299,
         301,  302,  309,  310,  312,  318,  320,  328,  330,  331,  333,
         337,  340,  345,  346,  347,  349,  350,  351,  354,  358,  365,
         369,  371,  372,  373,  374,  377,  380,  381,  383,  387,  388,
         389,  393,  396,  397,  399,  402,  404,  409,  413,  419,  428,
         430,  431,  437,  438,  441,  449,  450,  454,  460,  461,  470,
         471,  474,  478,  481,  483,  485,  486,  487,  488,  490,  493,
         494,  496,  499,  500,  505,  507,  511,  512,  515,  516,  520,
         522,  539,  542,  543,  546,  548,  549,  550,  555,  556,  557,
         559,  560,  565,  570,  571,  572,  574,  575,  576,  577,  579,
         580,  583,  586,  596,  597,  599,  601,  603,  605,  606,  613,
         615,  617,  618,  620,  622,  623,  626,  628,  629,  632,  634,
         637,  638,  639,  640,  642,  643,  644,  648,  650,  654,  655,
         657,  659,  665,  668,  670,  671,  672,  673,  678,  679,  680,
         684,  691,  694,  697,  698,  703,  712,  713,  715,  717,  719,
         722,  724,  726,  727,  734,  735,  737,  739,  742,  747,  754,
         759,  763,  765,  767,  769,  772,  781,  782,  786,  790,  793,
         794,  795,  802,  803,  811,  815,  816,  818,  819,  820,  822,
         823,  824,  826,  827,  830,  832,  833,  835,  839,  840,  845,
         848,  850,  854,  856,  859,  862,  863,  865,  866,  868,  876,
         877,  878,  884,  885,  893,  895,  901,  902,  903,  906,  909,
         917,  921,  924,  925,  929,  931,  933,  934,  938,  957,  961,
         965,  970,  972,  973,  982,  984,  986,  988,  989,  990,  991,
         993,  996,  998, 1000, 1001, 1002, 1003, 1004, 1011, 1013, 1015,
        1017, 1019, 1020, 1022, 1025, 1026, 1027, 1028, 1035, 1037, 1038,
        1041, 1042, 1045, 1046, 1047, 1049, 1051, 1052, 1059, 1061, 1065,
        1066, 1067, 1069, 1070, 1072, 1074, 1079, 1082, 1088, 1091, 1097,
        1098, 1101, 1104, 1105, 1107, 1108, 1109, 1113, 1115, 1117, 1120,
        1121, 1123, 1125, 1127, 1128, 1132, 1133, 1134, 1137, 1139, 1141,
        1144, 1145, 1152, 1158, 1161, 1168, 1169, 1170, 1172, 1173, 1178,
        1180, 1182, 1189, 1191, 1192, 1197, 1199, 1200, 1202, 1207, 1211,
        1215, 1217, 1219, 1224, 1226, 1227, 1228, 1229, 1230, 1233, 1234,
        1238, 1241, 1245, 1247, 1248, 1250, 1252, 1254, 1256, 1258, 1259,
        1261, 1270, 1272, 1276, 1283, 1285, 1286, 1287, 1292, 1293, 1294,
        1299, 1302, 1306, 1309, 1311, 1317, 1319, 1321, 1323, 1324, 1325,
        1329, 1335, 1337, 1342, 1343, 1345, 1349, 1352, 1355, 1358, 1359,
        1360, 1362, 1366, 1367, 1371, 1376, 1378, 1380, 1381, 1382, 1387,
        1388, 1391, 1397, 1400, 1402, 1406, 1407, 1408, 1411, 1413, 1415,
        1417, 1419, 1422, 1423, 1426, 1427, 1428, 1435, 1436, 1440, 1448,
        1449, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1464, 1465,
        1469]),)

cluster_0 = final_log.iloc[index_0]
cluster_0

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
3	0	1	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.835167	0.0	-1.018674	-0.374906	0.338096	0.555462	0.882230	-1.191138
4	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.660400	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-0.596855
10	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.986172	0.0	0.510149	-0.691946	0.338096	-0.216025	-0.018341	-0.299713
12	0	0	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.834541	0.0	0.510149	-0.850466	-1.077862	-0.216025	-0.618722	-0.299713
13	0	0	1	0	0	0	0	1	0	0	...	-0.961486	1.153254	-0.912702	0.0	0.510149	-1.167505	0.338096	-0.987512	-0.618722	-0.596855
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1459	0	0	1	0	0	0	0	1	0	0	...	-0.057788	-0.660853	-0.486259	0.0	0.510149	-0.057867	0.338096	-0.473188	-0.318532	-0.299713
1460	0	0	1	0	0	0	0	0	0	0	...	-0.961486	-1.567907	-0.561293	0.0	-1.018674	-0.850466	-2.493820	-0.216025	-0.018341	-0.002572
1464	0	0	0	1	0	0	0	0	0	0	...	-0.961486	0.246200	-0.817346	0.0	-1.018674	-0.850466	0.338096	-0.473188	-0.618722	-1.191138
1465	0	1	1	0	0	0	0	1	0	0	...	-0.057788	1.153254	-0.940839	0.0	0.510149	1.051772	0.338096	-0.216025	-0.618722	-0.299713
1469	0	0	1	0	0	0	0	1	0	0	...	-0.057788	0.246200	-0.367768	0.0	-1.018674	-0.691946	1.754054	-0.473188	-0.318532	-0.596855

529 rows × 29 columns

# cluster=1

index_1 = np.where(C_var==1)
index_1

(array([  18,   25,   28,   29,   45,   62,   63,   65,   67,   70,   77,
          78,   82,   85,   90,   93,   95,   98,  106,  112,  123,  126,
         131,  133,  136,  147,  163,  165,  186,  187,  189,  190,  209,
         213,  215,  218,  219,  231,  233,  235,  237,  244,  245,  257,
         263,  268,  270,  271,  275,  276,  279,  280,  290,  295,  300,
         307,  308,  313,  314,  316,  326,  329,  332,  348,  360,  367,
         376,  379,  390,  392,  400,  401,  406,  408,  411,  417,  420,
         424,  425,  427,  429,  433,  435,  448,  455,  458,  464,  465,
         466,  473,  477,  489,  492,  497,  510,  534,  535,  536,  538,
         541,  544,  552,  561,  568,  584,  588,  592,  595,  616,  624,
         625,  646,  649,  653,  674,  677,  695,  699,  701,  706,  716,
         721,  723,  728,  736,  738,  741,  743,  750,  753,  755,  758,
         766,  770,  771,  774,  775,  787,  789,  799,  806,  810,  812,
         813,  814,  821,  838,  851,  858,  869,  887,  890,  894,  897,
         898,  899,  904,  905,  907,  913,  914,  916,  919,  922,  926,
         937,  945,  954,  955,  956,  962,  966,  971,  975,  976,  987,
         994,  999, 1008, 1010, 1014, 1024, 1031, 1034, 1043, 1044, 1054,
        1062, 1076, 1078, 1080, 1086, 1093, 1096, 1111, 1126, 1135, 1138,
        1140, 1154, 1164, 1176, 1177, 1194, 1195, 1196, 1203, 1223, 1225,
        1235, 1242, 1264, 1268, 1275, 1277, 1301, 1303, 1305, 1310, 1327,
        1331, 1348, 1351, 1370, 1374, 1377, 1396, 1401, 1403, 1437, 1443,
        1461, 1462]),)

cluster_1 = final_log.iloc[index_1]
cluster_1

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
18	0	0	0	1	0	0	0	0	1	0	...	1.749610	1.153254	3.078475	0.0	-1.018674	0.144974	0.338096	0.300399	1.182421	0.888852
25	0	0	1	0	0	0	0	0	1	0	...	2.653309	0.246200	0.288444	0.0	0.510149	2.478450	-1.077862	2.098437	2.683373	1.185994
28	0	0	1	0	0	0	1	0	0	0	...	0.845911	1.153254	1.459306	0.0	0.510149	2.161410	0.338096	0.300399	0.582040	0.034015
29	0	0	0	1	1	0	0	0	1	0	...	2.653309	-1.567907	0.288444	0.0	-1.018674	1.844371	-1.077862	-0.987512	-0.618722	-0.893996
45	0	0	1	0	0	1	0	0	0	0	...	2.653309	0.246200	0.288444	0.0	-1.018674	2.002890	0.338096	0.300399	0.050478	1.185994
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1403	0	0	0	1	1	0	0	0	0	0	...	1.749610	-1.567907	2.426305	0.0	-1.018674	1.685851	0.338096	0.300399	1.182421	1.780277
1437	1	0	1	0	0	0	0	0	1	0	...	2.653309	1.153254	0.288444	0.0	-1.018674	1.685851	-1.077862	0.041137	-1.219103	-0.299713
1443	0	0	1	0	0	0	0	0	1	0	...	2.653309	0.246200	0.288444	0.0	-1.018674	2.161410	-1.077862	0.300399	0.582040	2.968843
1461	0	0	0	1	1	0	0	0	0	0	...	0.845911	-1.567907	1.648767	0.0	0.510149	1.527331	0.338096	-0.730350	-0.618722	-1.191138
1462	0	0	0	1	1	0	0	0	0	0	...	1.749610	1.153254	2.016746	0.0	0.510149	1.685851	-1.077862	0.300399	1.482611	0.591711

233 rows × 29 columns

# cluster=2

index_2 = np.where(C_var==2)
index_2

(array([   5,    9,   11,   15,   22,   27,   43,   55,   59,   61,   64,
          73,   76,   80,   83,   88,   89,   92,   94,  103,  116,  117,
         119,  121,  124,  129,  137,  139,  144,  150,  151,  153,  154,
         155,  158,  166,  167,  172,  173,  185,  188,  195,  199,  201,
         210,  212,  220,  222,  223,  226,  227,  228,  243,  247,  256,
         261,  269,  282,  283,  285,  291,  293,  297,  303,  304,  305,
         306,  311,  315,  317,  319,  321,  322,  324,  334,  338,  339,
         341,  342,  343,  344,  353,  355,  359,  361,  366,  384,  386,
         394,  398,  403,  412,  423,  426,  432,  442,  444,  446,  447,
         452,  459,  462,  467,  468,  472,  491,  503,  506,  508,  514,
         519,  523,  524,  526,  527,  529,  530,  531,  532,  533,  551,
         554,  558,  562,  564,  569,  578,  590,  593,  600,  604,  607,
         608,  610,  621,  635,  636,  641,  647,  652,  658,  664,  675,
         681,  685,  686,  690,  692,  693,  696,  702,  704,  705,  707,
         708,  710,  718,  729,  730,  733,  745,  749,  751,  752,  756,
         757,  760,  768,  773,  779,  780,  783,  784,  788,  796,  805,
         807,  809,  825,  836,  837,  844,  846,  852,  855,  870,  872,
         873,  874,  879,  881,  883,  888,  889,  891,  896,  900,  908,
         920,  923,  927,  928,  930,  932,  935,  941,  942,  943,  944,
         947,  949,  950,  951,  958,  959,  960,  963,  964,  968,  969,
         979,  983,  985,  995,  997, 1005, 1007, 1018, 1029, 1030, 1033,
        1040, 1048, 1050, 1055, 1063, 1073, 1081, 1084, 1085, 1089, 1090,
        1094, 1095, 1099, 1103, 1106, 1114, 1119, 1122, 1124, 1130, 1131,
        1142, 1143, 1146, 1147, 1148, 1149, 1150, 1155, 1156, 1157, 1159,
        1160, 1162, 1163, 1165, 1174, 1179, 1181, 1187, 1188, 1190, 1204,
        1208, 1210, 1212, 1214, 1216, 1218, 1220, 1231, 1232, 1240, 1244,
        1251, 1253, 1260, 1265, 1267, 1269, 1274, 1278, 1280, 1281, 1282,
        1288, 1289, 1295, 1296, 1304, 1318, 1322, 1330, 1334, 1340, 1341,
        1346, 1350, 1357, 1361, 1363, 1364, 1368, 1373, 1385, 1386, 1392,
        1393, 1395, 1399, 1404, 1409, 1410, 1412, 1416, 1418, 1421, 1424,
        1425, 1429, 1430, 1431, 1434, 1439, 1444, 1450, 1451, 1463, 1466,
        1468]),)

cluster_2 = final_log.iloc[index_2]
cluster_2

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
5	0	1	1	0	0	0	0	1	0	0	...	-0.961486	1.153254	-0.785457	0.0	-1.018674	-0.374906	-1.077862	0.298300	0.882230	0.591711
9	0	0	1	0	0	0	1	0	0	0	...	-0.057788	0.246200	-0.107338	0.0	2.038972	1.051772	-1.077862	0.298300	0.882230	0.888852
11	0	0	1	0	0	0	0	1	0	0	...	-0.057788	0.246200	-0.433736	0.0	-1.018674	-0.057867	0.338096	0.812625	0.281849	1.185994
15	0	0	1	0	0	0	0	0	0	1	...	0.845911	-1.567907	1.375519	0.0	0.510149	-0.057867	0.338096	1.069787	1.482611	1.185994
22	0	0	1	0	0	0	0	0	0	0	...	0.845911	-0.660853	2.005178	0.0	-1.018674	0.417693	0.338096	1.584112	0.582040	2.077418
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1450	0	0	0	0	0	0	0	0	0	0	...	0.845911	1.153254	1.018170	0.0	-1.018674	-0.216386	0.338096	0.812625	-1.219103	0.888852
1451	0	0	0	1	0	0	0	0	0	0	...	-0.057788	1.153254	-0.074198	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	1.483135
1463	1	0	1	0	0	0	0	0	0	1	...	-0.057788	-1.567907	1.361762	0.0	-1.018674	-0.057867	0.338096	0.812625	-0.018341	0.888852
1466	0	0	1	0	0	0	1	0	0	0	...	0.845911	-1.567907	1.378958	0.0	0.510149	-0.216386	0.338096	0.298300	0.882230	0.888852
1468	0	1	0	1	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.059504	0.0	-1.018674	1.051772	-1.077862	0.812625	0.582040	1.185994

342 rows × 29 columns

# Clusters = 3

index_3 = np.where(C_var==3)
index_3

(array([   0,    2,   14,   24,   33,   34,   36,   42,   50,   51,   69,
         102,  107,  122,  127,  132,  171,  177,  192,  204,  214,  216,
         217,  229,  234,  239,  250,  259,  264,  288,  296,  323,  327,
         336,  357,  363,  368,  370,  378,  385,  405,  414,  415,  421,
         422,  436,  439,  440,  443,  453,  457,  463,  469,  479,  480,
         482,  495,  504,  513,  525,  528,  540,  547,  566,  573,  585,
         589,  591,  598,  614,  645,  656,  660,  662,  663,  666,  667,
         683,  688,  689,  709,  711,  720,  731,  732,  744,  748,  761,
         762,  776,  777,  797,  798,  800,  801,  828,  829,  831,  842,
         849,  857,  860,  864,  871,  892,  911,  915,  939,  940,  946,
         952,  953,  980,  981, 1012, 1016, 1021, 1032, 1036, 1039, 1056,
        1057, 1060, 1068, 1077, 1083, 1110, 1112, 1136, 1153, 1167, 1171,
        1201, 1205, 1213, 1222, 1236, 1237, 1246, 1249, 1255, 1257, 1262,
        1271, 1273, 1279, 1290, 1291, 1297, 1312, 1313, 1326, 1332, 1338,
        1339, 1354, 1365, 1369, 1375, 1379, 1390, 1438, 1442, 1452]),)

# cluster=4

index_4 =np.where(C_var==4)
index_4

(array([   1,    6,    7,    8,   21,   26,   44,   46,   47,   48,   52,
          56,   58,   60,   71,   75,   79,   87,   91,   96,  100,  105,
         110,  111,  118,  120,  134,  135,  140,  142,  148,  152,  157,
         160,  168,  169,  178,  180,  182,  194,  205,  211,  221,  236,
         251,  260,  266,  272,  277,  278,  281,  286,  289,  325,  335,
         352,  356,  362,  364,  375,  382,  391,  395,  407,  410,  416,
         418,  434,  445,  451,  456,  475,  476,  484,  498,  501,  502,
         509,  517,  518,  521,  537,  545,  553,  563,  567,  581,  582,
         587,  594,  602,  609,  611,  612,  619,  627,  630,  631,  633,
         651,  661,  669,  676,  682,  687,  700,  714,  725,  740,  746,
         764,  778,  785,  791,  792,  804,  808,  817,  834,  841,  843,
         847,  853,  861,  867,  875,  880,  882,  886,  910,  912,  918,
         936,  948,  967,  974,  977,  978,  992, 1006, 1009, 1023, 1053,
        1058, 1064, 1071, 1075, 1087, 1092, 1100, 1102, 1116, 1118, 1129,
        1151, 1166, 1175, 1183, 1184, 1185, 1186, 1193, 1198, 1206, 1209,
        1221, 1239, 1243, 1263, 1266, 1284, 1298, 1300, 1307, 1308, 1314,
        1315, 1316, 1320, 1328, 1333, 1336, 1344, 1347, 1353, 1356, 1372,
        1383, 1384, 1389, 1394, 1398, 1405, 1414, 1420, 1432, 1433, 1441,
        1445, 1446, 1447, 1467]),)

cluster_4 = final_log.iloc[index_4]
cluster_4

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
1	0	1	1	0	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.140791	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	0.888852
6	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-1.567907	-0.909888	0.0	0.195024	0.259173	-1.077862	-1.244675	-1.219103	-1.191138
7	0	0	1	0	0	0	0	1	0	0	...	-0.961486	0.246200	-0.902697	0.0	0.510149	-1.484545	0.338096	-1.244675	-1.219103	-1.191138
8	0	1	1	0	0	0	0	0	0	1	...	0.845911	0.246200	1.233580	0.0	-1.018674	-0.057867	0.338096	0.812625	0.882230	1.185994
21	0	0	0	1	0	0	0	0	0	0	...	-0.961486	-1.567907	-0.679471	0.0	-1.018674	-0.057867	0.338096	-0.216025	-0.318532	-0.299713
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1441	1	0	1	0	0	0	1	0	0	0	...	-0.057788	0.246200	0.226875	0.0	0.510149	0.417693	-1.077862	1.841275	2.383182	1.483135
1445	0	0	1	0	0	0	0	0	0	1	...	1.749610	-0.660853	2.497900	0.0	0.510149	1.685851	0.338096	0.300399	0.882230	1.780277
1446	0	0	0	1	1	0	0	0	0	0	...	-0.057788	0.246200	0.353807	0.0	2.038972	-0.374906	0.338096	0.555462	0.882230	0.888852
1447	1	0	0	1	1	0	0	0	0	0	...	-0.057788	1.153254	-0.054502	0.0	0.510149	0.734732	-1.077862	2.355600	2.383182	2.077418
1467	0	0	1	0	0	0	0	0	0	1	...	-0.057788	-0.660853	0.175602	0.0	0.510149	-0.691946	0.338096	0.041137	-0.618722	-0.299713

202 rows × 29 columns

WCSS_list1 = []
for k in range(1,10):
    kmeans_model = KMeans(n_clusters=k)  
    kmeans_model.fit(x_log)
    WCSS = kmeans_model.inertia_
    WCSS_list1.append(WCSS)
    print('K >>',k,'WCSS>>',WCSS)

K >> 1 WCSS>> 36749.99999999997
K >> 2 WCSS>> 32432.28022057883
K >> 3 WCSS>> 30728.82109411982
K >> 4 WCSS>> 29482.758915733095
K >> 5 WCSS>> 28490.18203277078
K >> 6 WCSS>> 27776.60942323769
K >> 7 WCSS>> 27144.416717879314
K >> 8 WCSS>> 26649.96132712636
K >> 9 WCSS>> 26359.154762882907

WCSS

26359.154762882907

# Elbow_method

k = range(1,10)
plt.plot(k,WCSS_list1)
plt.xlabel('Number of clusters(K)')
plt.ylabel('WCSS')
plt.title('Elbow method graph')

Text(0.5, 1.0, 'Elbow method graph')

# Predicted = 2

kmeans_model = KMeans(n_clusters=2)
kmeans_model.fit(x_log)

KMeans(n_clusters=2)

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kmeans_model = KMeans(n_clusters=2)  # n_clusters=8  >> default
pred_k=kmeans_model.fit_predict(x_log)

pred_k

array([1, 0, 1, ..., 1, 0, 1], dtype=int32)

final_log

	BusinessTravel_Non-Travel	BusinessTravel_Travel_Frequently	Department_Research & Development	Department_Sales	EducationField_Marketing	EducationField_Technical Degree	JobRole_Healthcare Representative	JobRole_Laboratory Technician	JobRole_Manager	JobRole_Manufacturing Director	...	JobLevel	JobSatisfaction	MonthlyIncome	StandardHours	StockOptionLevel	TotalWorkingYears	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsWithCurrManager
0	0	0	0	1	0	0	0	0	0	0	...	-0.057788	1.153254	0.129018	0.0	-1.018674	-0.374906	-2.493820	0.041137	-0.018341	0.294570
1	0	1	1	0	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.140791	0.0	0.510149	-0.057867	0.338096	1.069787	0.882230	0.888852
2	0	0	1	0	0	0	0	1	0	0	...	-0.961486	0.246200	-1.091220	0.0	-1.018674	-0.533426	0.338096	-1.501837	-1.219103	-1.191138
3	0	1	1	0	0	0	0	0	0	0	...	-0.961486	0.246200	-0.835167	0.0	-1.018674	-0.374906	0.338096	0.555462	0.882230	-1.191138
4	0	0	1	0	0	0	0	1	0	0	...	-0.961486	-0.660853	-0.660400	0.0	0.510149	-0.691946	0.338096	-0.987512	-0.618722	-0.596855
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	0	1	1	0	0	0	0	1	0	0	...	-0.057788	1.153254	-0.940839	0.0	0.510149	1.051772	0.338096	-0.216025	-0.618722	-0.299713
1466	0	0	1	0	0	0	1	0	0	0	...	0.845911	-1.567907	1.378958	0.0	0.510149	-0.216386	0.338096	0.298300	0.882230	0.888852
1467	0	0	1	0	0	0	0	0	0	1	...	-0.057788	-0.660853	0.175602	0.0	0.510149	-0.691946	0.338096	0.041137	-0.618722	-0.299713
1468	0	1	0	1	0	0	0	0	0	0	...	-0.057788	-0.660853	-0.059504	0.0	-1.018674	1.051772	-1.077862	0.812625	0.582040	1.185994
1469	0	0	1	0	0	0	0	1	0	0	...	-0.057788	0.246200	-0.367768	0.0	-1.018674	-0.691946	1.754054	-0.473188	-0.318532	-0.596855

1470 rows × 29 columns

Principle Component analysis

from sklearn.decomposition import PCA

pca = PCA(n_components=2)

pca.fit(x_log)

PCA(n_components=2)

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x_pca = pca.transform(x_log)

x_pca

array([[-0.49360165,  1.41619175],
       [ 1.15378029, -1.55229841],
       [-3.03698632,  1.36126884],
       ...,
       [-0.98841934, -1.0507973 ],
       [ 1.47493103,  0.30399305],
       [-1.06652057,  0.13379506]])

# normalization of pca due to noise in clustering

x_pca_scaler = StandardScaler().fit(x_pca)

x_pca1 = x_pca_scaler.transform(x_pca)

x_pca1

array([[-0.24458175,  1.0159896 ],
       [ 0.57170313, -1.11363383],
       [-1.50483988,  0.97658738],
       ...,
       [-0.48976606, -0.7538521 ],
       [ 0.73083471,  0.21808754],
       [-0.52846556,  0.09598586]])

H = pd.DataFrame(x_pca1)
H

	0	1
0	-0.244582	1.015990
1	0.571703	-1.113634
2	-1.504840	0.976587
3	-0.257197	-0.558017
4	-0.981771	-0.244826
...	...	...
1465	-0.239128	0.387034
1466	0.702694	0.004628
1467	-0.489766	-0.753852
1468	0.730835	0.218088
1469	-0.528466	0.095986

1470 rows × 2 columns

L = H.rename({0:'X1',1:'Y1'},axis=1)

L

	X1	Y1
0	-0.244582	1.015990
1	0.571703	-1.113634
2	-1.504840	0.976587
3	-0.257197	-0.558017
4	-0.981771	-0.244826
...	...	...
1465	-0.239128	0.387034
1466	0.702694	0.004628
1467	-0.489766	-0.753852
1468	0.730835	0.218088
1469	-0.528466	0.095986

1470 rows × 2 columns

# Visualaisation of Clustering

plt.scatter(L['X1'],L['Y1'],c=pred_k,s=30)
plt.title("visualisation of K-means")
plt.xlabel("X-AXIS")
plt.ylabel("Y-AXIS")
plt.show()

from sklearn.metrics import silhouette_score

silhouette_score(L,pred_k)

0.3733471110993441

2. Heirarchical Clustering

# Dendrogram

import scipy.cluster.hierarchy as sch
plt.figure(figsize=(10,9))

dendrogram = sch.dendrogram(sch.linkage(L,method='ward'))
plt.title('dendrogram')
plt.xlabel('X-AXIS')
plt.ylabel('Euclidean distances')
plt.show()

from sklearn.cluster import AgglomerativeClustering
cluster = AgglomerativeClustering(n_clusters=2,affinity = 'euclidean',linkage='ward')

cluster.fit_predict(L)

array([0, 1, 0, ..., 0, 1, 0])

XC = cluster.fit_predict(L)

from sklearn.metrics import silhouette_score

silhouette_score(L,XC)

0.38182922187276147

# Visualisation of clustering 

plt.scatter(L['X1'],L['Y1'],c=XC,s=30)
plt.title("Visualisation of Heirarchical clustering")
plt.xlabel("X-AXIS")
plt.ylabel("Y-AXIS")
plt.show()

3] DBSCAN Clustering

from sklearn.cluster import DBSCAN

A = DBSCAN (eps=3.6,min_samples=8)

labels = A.fit_predict(x_log)
np.unique(labels)

array([-1,  0,  1,  2])

labels = A.fit_predict(x_log)

np.unique(labels)

array([-1,  0,  1,  2])

# VISUALISATION OF CLUSTER 

plt.scatter(L['X1'],L['Y1'],c=labels,s=30)
plt.xlabel("X-AXIS")
plt.ylabel("Y-AXIS")
plt.title("Visualisation of DBSCAN ")
plt.show()

silhouette_score(L,labels)

-0.12086921127977733